Abstract

A set of measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In other embodiment, helper networks can be used to filter identification inputs to improve the accuracy of the models that use encrypted inputs for classification.

IPC Classes  ?

• G06F 7/02 - Comparing digital values
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06F 18/213 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06N 3/08 - Learning methods
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06V 10/772 - Determining representative reference patterns, e.g. averaging or distorting patternsGenerating dictionaries
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
• G06V 40/12 - Fingerprints or palmprints
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/40 - Spoof detection, e.g. liveness detection

Abstract

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.

IPC Classes  ?

• G06F 7/02 - Comparing digital values
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06V 10/772 - Determining representative reference patterns, e.g. averaging or distorting patternsGenerating dictionaries
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
• G06V 40/12 - Fingerprints or palmprints
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/40 - Spoof detection, e.g. liveness detection
• G06V 40/70 - Multimodal biometrics, e.g. combining information from different biometric modalities
• H04L 9/40 - Network security protocols

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• G06F 18/2411 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
• G06F 18/25 - Fusion techniques
• G06N 3/045 - Combinations of networks
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 40/12 - Fingerprints or palmprints
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/50 - Maintenance of biometric data or enrolment thereof
• G06V 40/70 - Multimodal biometrics, e.g. combining information from different biometric modalities

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) an authentication system can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption of the encrypted feature vectors. Security of such privacy enable biometrics can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted biometric has not been spoofed or faked.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06V 40/40 - Spoof detection, e.g. liveness detection

Abstract

A set of distance measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In another embodiment, distance measurable or homomorphic encryption enables computations and comparisons on cypher-text without decryption of the encrypted feature vectors. Security of such privacy enabled embeddings can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted credential has not been spoofed or faked.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/045 - Combinations of networks
• G06N 3/08 - Learning methods

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06F 18/2135 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
• G06F 21/60 - Protecting data
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06N 3/02 - Neural networks
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• H04L 9/40 - Network security protocols

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) an authentication system can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption of the encrypted feature vectors. Security of such privacy enable biometrics can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted biometric has not been spoofed or faked.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06N 3/045 - Combinations of networks
• G06N 3/08 - Learning methods
• G06N 20/00 - Machine learning
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 30/194 - References adjustable by an adaptive method, e.g. learning
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols

Abstract

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06V 10/772 - Determining representative reference patterns, e.g. averaging or distorting patternsGenerating dictionaries
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
• G06V 40/12 - Fingerprints or palmprints
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/40 - Spoof detection, e.g. liveness detection
• H04L 9/40 - Network security protocols

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols

Abstract

A private identity system performs on-device embedding and homomorphic tokenization (HT) to map plaintext inputs to non-invertible HT tokens. During enrollment, HT tokens are stored and a centroid can be computed for each user. During prediction, a new HT token shortlists nearest centroids, followed by 1:1 distance verification against stored embeddings. On success, the system returns a UUID bound to the user. Because passkeys can be shared via device or account keychains, the UUID binds the ceremony to the same enrolled individual without exposing biometrics.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods
• G06V 40/40 - Spoof detection, e.g. liveness detection

Abstract

A set of distance measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In another embodiment, distance measurable or homomorphic encryption enables computations and comparisons on cypher-text without decryption of the encrypted feature vectors. Security of such privacy enabled embeddings can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted credential has not been spoofed or faked.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods

Abstract

Systems and methods of authorizing access to access-controlled environments are provided. In one example, a method includes receiving, passively by a computing device, user behavior authentication information indicative of a behavior of a user of the computing device, comparing, by the computing device, the user behavior authentication information to a stored user identifier associated with the user, calculating, by the computing device, a user identity probability based on the comparison of the user behavior authentication information to the stored user identifier, receiving, by the computing device, a request from the user to execute an access-controlled function, and granting, by the computing device, the request from the user responsive to determining that the user identity probability satisfies a first identity probability threshold associated with the access-controlled function.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints

IPC Classes  ?

• G06F 16/13 - File access structures, e.g. distributed indices
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 21/31 - User authentication
• G06N 3/08 - Learning methods
• G06V 40/50 - Maintenance of biometric data or enrolment thereof
• H04L 9/08 - Key distribution
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• H04L 9/40 - Network security protocols

Abstract

A private identity system is configured to invoke an embedding layer that constructs encoded embeddings from plaintext identification inputs using pre-trained models. Identification inputs can be captured (e.g., biometric, face image, retinal scan, fingerprint, voice, health data, behavioral, etc.) in plaintext versions and transformed into encoded embeddings. The embeddings can be produced to be homomorphic one-way encryptions of the plaintext identification instances. According to some embodiments, the encoded embeddings can be used to identify an entity. The encoded embeddings can be stored in a vector database during enrollment, retrieved by querying the vector database during prediction, and used to establish a match to an identifier, identity, and/or entity. In further embodiments, vector indexes can be used to speed queries executed on the vector database, and achieve improved computation, reduced query speed, and improved identification latency relative to multiple AI model architectures (e.g., embedding and classifier architectures).

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system

Abstract

A private identity system is configured to invoke an embedding layer that constructs encoded embeddings from plaintext identification inputs using pre-trained models. Identification inputs can be captured (e.g., biometric, face image, retinal scan, fingerprint, voice, health data, behavioral, etc.) in plaintext versions and transformed into encoded embeddings. The embeddings can be produced to be homomorphic one-way encryptions of the plaintext identification instances. According to some embodiments, the encoded embeddings can be used to identify an entity. The encoded embeddings can be stored in a vector database during enrollment, retrieved by querying the vector database during prediction, and used to establish a match to an identifier, identity, and/or entity. In further embodiments, vector indexes can be used to speed queries executed on the vector database, and achieve improved computation, reduced query speed, and improved identification latency relative to multiple Al model architectures (e.g., embedding and classifier architectures).

IPC Classes  ?

• G06V 40/50 - Maintenance of biometric data or enrolment thereof
• G06F 21/31 - User authentication
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• H04L 9/08 - Key distribution
• G06N 3/08 - Learning methods
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/13 - File access structures, e.g. distributed indices
• H04L 9/40 - Network security protocols

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) an authentication system can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption of the encrypted feature vectors. Security of such privacy enable biometrics can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted biometric has not been spoofed or faked.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06N 3/045 - Combinations of networks
• G06N 3/08 - Learning methods
• G06N 20/00 - Machine learning
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 30/194 - References adjustable by an adaptive method, e.g. learning
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols

Abstract

In various embodiments, a fully encrypted private identity based on biometric and/or behavior information can be used to securely identify any user efficiently. According to various aspects, once identification is secure and computationally efficient, the secure identity/identifier can be used across any number of devices to identify a user an enable functionality on any device based on the underlying identity, and even switch between identified users seamlessly all with little overhead. In some embodiments, devices can be configured to operate with function sets that transition seamlessly between the identified users, even, for example, as they pass a single mobile device back and forth. According to some embodiments, identification can extend beyond the current user of any device, into identification of actors responsible for activity/content on the device.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods
• G06V 40/40 - Spoof detection, e.g. liveness detection
• G06V 40/50 - Maintenance of biometric data or enrolment thereof
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system

Abstract

In various embodiments, a fully encrypted private identity based on biometric and/or behavior information can be used to securely identify any user efficiently. According to various aspects, once identification is secure and computationally efficient, the secure identity/identifier can be used across any number of devices to identify a user an enable functionality on any device based on the underlying identity, and even switch between identified users seamlessly all with little overhead. In some embodiments, devices can be configured to operate with function sets that transition seamlessly between the identified users, even, for example, as they pass a single mobile device back and forth. According to some embodiments, identification can extend beyond the current user of any device, into identification of actors responsible for activity/content on the device.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods
• G06V 40/40 - Spoof detection, e.g. liveness detection
• G06V 40/50 - Maintenance of biometric data or enrolment thereof
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system

Abstract

Systems and methods of authorizing access to access-controlled environments are provided. In one example, a method includes receiving, passively by a computing device, user behavior authentication information indicative of a behavior of a user of the computing device, comparing, by the computing device, the user behavior authentication information to a stored user identifier associated with the user, calculating, by the computing device, a user identity probability based on the comparison of the user behavior authentication information to the stored user identifier, receiving, by the computing device, a request from the user to execute an access-controlled function, and granting, by the computing device, the request from the user responsive to determining that the user identity probability satisfies a first identity probability threshold associated with the access-controlled function.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints

Abstract

A set of measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In other embodiment, helper networks can be used to filter identification inputs to improve the accuracy of the models that use encrypted inputs for classification.

IPC Classes  ?

• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06F 7/02 - Comparing digital values
• G06F 18/213 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06N 3/08 - Learning methods
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06V 10/772 - Determining representative reference patterns, e.g. averaging or distorting patternsGenerating dictionaries
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
• G06V 40/12 - Fingerprints or palmprints
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/40 - Spoof detection, e.g. liveness detection

Abstract

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06V 10/772 - Determining representative reference patterns, e.g. averaging or distorting patternsGenerating dictionaries
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
• G06V 40/12 - Fingerprints or palmprints
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/40 - Spoof detection, e.g. liveness detection

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) an authentication system can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption of the encrypted feature vectors. Security of such privacy enable biometrics can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted biometric has not been spoofed or faked.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods
• G06V 40/40 - Spoof detection, e.g. liveness detection

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 18/2135 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06F 21/60 - Protecting data
• G06N 3/02 - Neural networks
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• H04L 9/40 - Network security protocols

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system

Abstract

A set of distance measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In another embodiment, distance measurable or homomorphic encryption enables computations and comparisons on cypher-text without decryption of the encrypted feature vectors. Security of such privacy enabled embeddings can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted credential has not been spoofed or faked.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/045 - Combinations of networks
• G06N 3/08 - Learning methods

Abstract

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06F 7/02 - Comparing digital values
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06V 10/772 - Determining representative reference patterns, e.g. averaging or distorting patternsGenerating dictionaries
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
• G06V 40/12 - Fingerprints or palmprints
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/40 - Spoof detection, e.g. liveness detection
• G06V 40/70 - Multimodal biometrics, e.g. combining information from different biometric modalities

Abstract

A set of distance measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In another embodiment, distance measurable or homomorphic encryption enables computations and comparisons on cypher-text without decryption of the encrypted feature vectors. Security of such privacy enabled embeddings can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted credential has not been spoofed or faked.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06N 3/08 - Learning methods
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• G06F 18/2411 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
• G06F 18/25 - Fusion techniques
• G06N 3/045 - Combinations of networks
• G06N 3/08 - Learning methods
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 40/12 - Fingerprints or palmprints
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/50 - Maintenance of biometric data or enrolment thereof
• G06V 40/70 - Multimodal biometrics, e.g. combining information from different biometric modalities

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) an authentication system can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption of the encrypted feature vectors. Security of such privacy enable biometrics can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted biometric has not been spoofed or faked.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06N 3/045 - Combinations of networks
• G06N 3/08 - Learning methods
• G06N 20/00 - Machine learning
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 30/194 - References adjustable by an adaptive method, e.g. learning
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols

Goods & Services

Downloadable computer software for biometric identification; biometric identification apparatus; downloadable computer programs for identity control, verification and enrollment based on biometric data; downloadable computer software that enables users to have an authenticated identity and identity credentials and documentation, and sensitive and/or confidential information and providing access thereto and/or transmission thereof using biometric authentication and/or multi-factor authentication User authentication technology services, namely, providing secure server and mobile device technology services using biometric features; providing user authentication services using biometric features, biometric authentication and/or multi-factor authentication technology, biometric hardware and software technology for e-commerce transactions, and biometric hardware and software technology for e-commerce, e-commerce transactions, enabling user provisioning and deprovisioning and managing access to identity credentials, identity information, identity documentation and verification of same and sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information, identity theft protection services, password management and emergency response; software as a service (SAAS) featuring software for providing information about computer security services information relating to the issuance of identity credentials, storage of identity information, identity protection, verification and transmission using biometric authentication and/or multi-factor authentication; Computer security services in the nature of administering a centralized computer system for issuing identity credentials and documentation, verifying identity credentials and documentation, storing identity credentials and documentation, encrypting identity credentials and documentation, transmitting identity credentials and documentation, and tracking and monitoring transmission of identity credentials and documentation for others using biometric authentication and/or multi-factor authentication technology; software as a service (SAAS) services featuring software for use in identification and verification of identity credentials, storage of identity information, identity protection, verification and transmission using biometric authentication and/or multi-factor authentication, administering a centralized computer system for issuing identity credentials and documentation, verifying identity credentials and documentation, storing identity credentials and documentation, encrypting identity credentials and documentation, transmitting identity credentials and documentation, and tracking and monitoring transmission of identity credentials and documentation for others using biometric authentication and/or multi-factor authentication technology, sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information; application service provider featuring application programming interface (API) software for use in identification and verification of identity credentials, storage of identity information, identity protection, verification and transmission using biometric authentication and/or multi-factor authentication, administering a centralized computer system for issuing identity credentials and documentation, verifying identity credentials and documentation, storing identity credentials and documentation, encrypting identity credentials and documentation, transmitting identity credentials and documentation, and tracking and monitoring transmission of identity credentials and documentation for others using biometric authentication and/or multi-factor authentication technology, sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information; platform as a service (PAAS) featuring computer software platforms for enabling user provisioning and deprovisioning and managing access to identity credentials, identity information, identity documentation and verification of same and sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information; Providing a website featuring online non-downloadable software that enables users to have the ability to have an authenticated identity and identity credentials and documentation, and sensitive and/or confidential information and providing access thereto and/or transmission thereof using biometric authentication and/or multi-factor authentication technology; providing user authentication services using biometric hardware and software technology for e-commerce transactions; providing user authentication services using biometric hardware and software technology for identity protection during e-commerce transactions; providing user authentication services using biometric hardware and software technology for identity theft protection services; providing user authentication services using biometric hardware and software technology for password management; providing user authentication services using biometric hardware and software technology for emergency response

Abstract

In various embodiments, a fully encrypted private identity based on biometric and/or behavior information can be used to securely identify any user efficiently. According to various aspects, once identification is secure and computationally efficient, the secure identity/identifier can be used across any number of devices to identify a user an enable functionality on any device based on the underlying identity, and even switch between identified users seamlessly all with little overhead. In some embodiments, devices can be configured to operate with function sets that transition seamlessly between the identified users, even, for example, as they pass a single mobile device back and forth. According to some embodiments, identification can extend beyond the current user of any device, into identification of actors responsible for activity/content on the device.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods
• G06V 40/40 - Spoof detection, e.g. liveness detection

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) an authentication system can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption of the encrypted feature vectors. Security of such privacy enable biometrics can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted biometric has not been spoofed or faked.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods
• G06V 40/40 - Spoof detection, e.g. liveness detection

Abstract

In various embodiments, a fully encrypted private identity based on biometric and/or behavior information can be used to securely identify any user efficiently. According to various aspects, once identification is secure and computationally efficient, the secure identity/identifier can be used across any number of devices to identify a user an enable functionality on any device based on the underlying identity, and even switch between identified users seamlessly all with little overhead. In some embodiments, devices can be configured to operate with function sets that transition seamlessly between the identified users, even, for example, as they pass a single mobile device back and forth. According to some embodiments, identification can extend beyond the current user of any device, into identification of actors responsible for activity/content on the device.

IPC Classes  ?

• G06F 21/31 - User authentication
• G06N 3/08 - Learning methods
• G06F 21/60 - Protecting data

Abstract

In various embodiments, a fully encrypted private identity based on biometric and/or behavior information can be used to securely identify any user efficiently. According to various aspects, once identification is secure and computationally efficient, the secure identity/identifier can be used across any number of devices to identify a user an enable functionality on any device based on the underlying identity, and even switch between identified users seamlessly all with little overhead. In some embodiments, devices can be configured to operate with function sets that transition seamlessly between the identified users, even, for example, as they pass a single mobile device back and forth. According to some embodiments, identification can extend beyond the current user of any device, into identification of actors responsible for activity/content on the device.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• G06V 40/50 - Maintenance of biometric data or enrolment thereof

Abstract

In various embodiments, a fully encrypted private identity based on biometric and/or behavior information can be used to securely identify any user efficiently. According to various aspects, once identification is secure and computationally efficient, the secure identity/identifier can be used across any number of devices to identify a user an enable functionality on any device based on the underlying identity, and even switch between identified users seamlessly all with little overhead. In some embodiments, devices can be configured to operate with function sets that transition seamlessly between the identified users, even, for example, as they pass a single mobile device back and forth. According to some embodiments, identification can extend beyond the current user of any device, into identification of actors responsible for activity/content on the device.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods
• G06V 40/40 - Spoof detection, e.g. liveness detection

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/02 - Neural networks
• H04L 9/40 - Network security protocols
• G06F 21/60 - Protecting data
• G06K 9/62 - Methods or arrangements for recognition using electronic means

Abstract

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• H04L 9/40 - Network security protocols
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/772 - Determining representative reference patterns, e.g. averaging or distorting patternsGenerating dictionaries
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
• G06V 40/12 - Fingerprints or palmprints
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/40 - Spoof detection, e.g. liveness detection

Abstract

Systems and methods of authorizing access to access-controlled environments are provided. In one example, a method includes receiving, passively by a computing device, user behavior authentication information indicative of a behavior of a user of the computing device, comparing, by the computing device, the user behavior authentication information to a stored user identifier associated with the user, calculating, by the computing device, a user identity probability based on the comparison of the user behavior authentication information to the stored user identifier, receiving, by the computing device, a request from the user to execute an access-controlled function, and granting, by the computing device, the request from the user responsive to determining that the user identity probability satisfies a first identity probability threshold associated with the access-controlled function.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints

Abstract

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.

IPC Classes  ?

• G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• G06N 20/00 - Machine learning

Abstract

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.

IPC Classes  ?

• G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• G06N 20/00 - Machine learning

Abstract

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06K 9/62 - Methods or arrangements for recognition using electronic means

Abstract

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06K 9/62 - Methods or arrangements for recognition using electronic means

Abstract

A set of measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In other embodiment, helper networks can be used to filter identification inputs to improve the accuracy of the models that use encrypted inputs for classification.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06N 3/08 - Learning methods
• G06F 7/02 - Comparing digital values
• G06F 18/213 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06V 10/772 - Determining representative reference patterns, e.g. averaging or distorting patternsGenerating dictionaries
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
• G06V 40/12 - Fingerprints or palmprints
• G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
• G06V 40/40 - Spoof detection, e.g. liveness detection

Abstract

(e.g.(e.g., liveness) to establish a submitted credential has not been spoofed or faked.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• G06N 20/00 - Machine learning

Abstract

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06K 9/62 - Methods or arrangements for recognition using electronic means

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network ("DNN") on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) an authentication system can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption of the encrypted feature vectors. Security of such privacy enable biometrics can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted biometric has not been spoofed or faked.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06K 9/46 - Extraction of features or characteristics of the image
• G06K 9/78 - Combination of image acquisition and recognition functions
• G06K 9/80 - Combination of image preprocessing and recognition functions
• H04W 12/06 - Authentication
• G06N 3/08 - Learning methods

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06N 3/08 - Learning methods
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints

Abstract

A set of distance measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In another embodiment, distance measurable or homomorphic encryption enables computations and comparisons on cypher-text without decryption of the encrypted feature vectors. Security of such privacy enabled embeddings can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted credential has not been spoofed or faked.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) an authentication system can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption of the encrypted feature vectors. Security of such privacy enable biometrics can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted biometric has not been spoofed or faked.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• G06K 9/46 - Extraction of features or characteristics of the image
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• G06N 20/00 - Machine learning
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06V 30/194 - References adjustable by an adaptive method, e.g. learning

Abstract

A set of distance measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In another embodiment, distance measurable or homomorphic encryption enables computations and comparisons on cypher-text without decryption of the encrypted feature vectors. Security of such privacy enabled embeddings can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted credential has not been spoofed or faked.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods

Abstract

Systems and methods of authorizing access to access-controlled environments are provided. In one example, a method includes receiving, passively by a computing device, user behavior authentication information indicative of a behavior of a user of the computing device, comparing, by the computing device, the user behavior authentication information to a stored user identifier associated with the user, calculating, by the computing device, a user identity probability based on the comparison of the user behavior authentication information to the stored user identifier, receiving, by the computing device, a request from the user to execute an access-controlled function, and granting, by the computing device, the request from the user responsive to determining that the user identity probability satisfies a first identity probability threshold associated with the access-controlled function.

IPC Classes  ?

• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints

Goods & Services

Downloadable computer software for biometric identification; biometric identification apparatus; downloadable computer programs for identity control, verification and enrollment based on biometric data; downloadable computer software that enables users to have an authenticated identity and identity credentials and documentation, and sensitive and/or confidential information and providing access thereto and/or transmission thereof using biometric authentication and/or multi-factor authentication User authentication technology services, namely, providing secure server and mobile device technology services using biometric features; providing user authentication services using biometric features, biometric authentication and/or multi-factor authentication technology, biometric hardware and software technology for e-commerce transactions, and biometric hardware and software technology for e-commerce, e-commerce transactions, enabling user provisioning and deprovisioning and managing access to identity credentials, identity information, identity documentation and verification of same and sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information, identity theft protection services, password management and emergency response; software as a service (SAAS) featuring software for providing information about computer security services information relating to the issuance of identity credentials, storage of identity information, identity protection, verification and transmission using biometric authentication and/or multi-factor authentication; Computer security services in the nature of administering a centralized computer system for issuing identity credentials and documentation, verifying identity credentials and documentation, storing identity credentials and documentation, encrypting identity credentials and documentation, transmitting identity credentials and documentation, and tracking and monitoring transmission of identity credentials and documentation for others using biometric authentication and/or multi-factor authentication technology; software as a service (SAAS) services featuring software for use in identification and verification of identity credentials, storage of identity information, identity protection, verification and transmission using biometric authentication and/or multi-factor authentication, administering a centralized computer system for issuing identity credentials and documentation, verifying identity credentials and documentation, storing identity credentials and documentation, encrypting identity credentials and documentation, transmitting identity credentials and documentation, and tracking and monitoring transmission of identity credentials and documentation for others using biometric authentication and/or multi-factor authentication technology, sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information ; application service provider featuring application programming interface (API) software for use in identification and verification of identity credentials, storage of identity information, identity protection, verification and transmission using biometric authentication and/or multi-factor authentication, administering a centralized computer system for issuing identity credentials and documentation, verifying identity credentials and documentation, storing identity credentials and documentation, encrypting identity credentials and documentation, transmitting identity credentials and documentation, and tracking and monitoring transmission of identity credentials and documentation for others using biometric authentication and/or multi-factor authentication technology, sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information; platform as a service (PAAS) featuring computer software platforms for enabling user provisioning and deprovisioning and managing access to identity credentials, identity information, identity documentation and verification of same and sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information; Providing a website featuring online software that enables users to have the ability to have an authenticated identity and identity credentials and documentation, and sensitive and/or confidential information and providing access thereto and/or transmission thereof using biometric authentication and/or multi-factor authentication technology; providing user authentication services using biometric hardware and software technology for e-commerce transactions; providing user authentication services using biometric hardware and software technology for any services; providing user authentication services using biometric hardware and software technology for identity theft protection services; providing user authentication services using biometric hardware and software technology for password management; providing user authentication services using biometric hardware and software technology for emergency response

Goods & Services

Downloadable computer software for biometric identification; biometric identification apparatus; downloadable computer programs for identity control, verification and enrollment based on biometric data; downloadable computer software that enables users to have an authenticated identity and identity credentials and documentation, and sensitive and/or confidential information and providing access thereto and/or transmission thereof using biometric authentication and/or multi-factor authentication User authentication technology services, namely, providing secure server and mobile device technology services using biometric features; providing user authentication services using biometric features, biometric authentication and/or multi-factor authentication technology, biometric hardware and software technology for e-commerce transactions, and biometric hardware and software technology for e-commerce, e-commerce transactions, enabling user provisioning and deprovisioning and managing access to identity credentials, identity information, identity documentation and verification of same and sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information, identity theft protection services, password management and emergency response; software as a service (SAAS) featuring software for providing information about computer security services information relating to the issuance of identity credentials, storage of identity information, identity protection, verification and transmission using biometric authentication and/or multi-factor authentication; Computer security services in the nature of administering a centralized computer system for issuing identity credentials and documentation, verifying identity credentials and documentation, storing identity credentials and documentation, encrypting identity credentials and documentation, transmitting identity credentials and documentation, and tracking and monitoring transmission of identity credentials and documentation for others using biometric authentication and/or multi-factor authentication technology; software as a service (SAAS) services featuring software for use in identification and verification of identity credentials, storage of identity information, identity protection, verification and transmission using biometric authentication and/or multi-factor authentication, administering a centralized computer system for issuing identity credentials and documentation, verifying identity credentials and documentation, storing identity credentials and documentation, encrypting identity credentials and documentation, transmitting identity credentials and documentation, and tracking and monitoring transmission of identity credentials and documentation for others using biometric authentication and/or multi-factor authentication technology, sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information ; application service provider featuring application programming interface (API) software for use in identification and verification of identity credentials, storage of identity information, identity protection, verification and transmission using biometric authentication and/or multi-factor authentication, administering a centralized computer system for issuing identity credentials and documentation, verifying identity credentials and documentation, storing identity credentials and documentation, encrypting identity credentials and documentation, transmitting identity credentials and documentation, and tracking and monitoring transmission of identity credentials and documentation for others using biometric authentication and/or multi-factor authentication technology, sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information; platform as a service (PAAS) featuring computer software platforms for enabling user provisioning and deprovisioning and managing access to identity credentials, identity information, identity documentation and verification of same and sensitive and/or confidential information, and transmission of identity information and sensitive and/or confidential information; Providing a website featuring online software that enables users to have the ability to have an authenticated identity and identity credentials and documentation, and sensitive and/or confidential information and providing access thereto and/or transmission thereof using biometric authentication and/or multi-factor authentication technology; providing user authentication services using biometric hardware and software technology for e-commerce transactions; providing user authentication services using biometric hardware and software technology for any services; providing user authentication services using biometric hardware and software technology for identity theft protection services; providing user authentication services using biometric hardware and software technology for password management; providing user authentication services using biometric hardware and software technology for emergency response

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06N 3/08 - Learning methods
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) an authentication system can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption of the encrypted feature vectors. Security of such privacy enable biometrics can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted biometric has not been spoofed or faked.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/08 - Learning methods

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06N 3/08 - Learning methods
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06N 3/08 - Learning methods
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06V 40/50 - Maintenance of biometric data or enrolment thereof

Abstract

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
• G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
• G06N 3/02 - Neural networks
• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• G06F 21/60 - Protecting data
• G06K 9/62 - Methods or arrangements for recognition using electronic means