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1. 发明授权

US11489866B2 Systems and methods for private authentication with helper networks 有权
公开(公告)号：US11489866B2
公开(公告)日：2022-11-01
申请号：US17398555
申请日：2021-08-10
申请人： Private Identity LLC
发明人： Scott Edward Streit
IPC分类号： H04L9/40 , G06N3/04 , G06K9/62
摘要： 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.

2. 发明申请

US20220100896A1 SYSTEMS AND METHODS FOR PRIVACY-ENABLED BIOMETRIC PROCESSING 有权
公开(公告)号：US20220100896A1
公开(公告)日：2022-03-31
申请号：US17492775
申请日：2021-10-04
申请人： Private Identity LLC
发明人： Scott Edward Streit
IPC分类号： G06F21/62 , H04L9/00 , G06F21/32 , G06N3/02 , H04L29/06 , G06F21/60 , G06K9/62
摘要： 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.

3. 发明申请

US20200228336A1 SYSTEMS AND METHODS FOR PRIVACY-ENABLED BIOMETRIC PROCESSING 审中-公开
公开(公告)号：US20200228336A1
公开(公告)日：2020-07-16
申请号：US16832014
申请日：2020-03-27
申请人： Private Identity LLC
发明人： Scott Edward Streit
IPC分类号： H04L9/32 , G06N3/08 , G06N3/04
摘要： 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.

4. 发明申请

US20220150068A1 SYSTEM AND METHODS FOR IMPLEMENTING PRIVATE IDENTITY 有权
公开(公告)号：US20220150068A1
公开(公告)日：2022-05-12
申请号：US17583687
申请日：2022-01-25
申请人： Private Identity LLC
发明人： Scott Edward Streit
IPC分类号： H04L9/32 , H04L9/00 , G06V40/50
摘要： 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.

5. 发明授权

US11265168B2 Systems and methods for privacy-enabled biometric processing 有权
公开(公告)号：US11265168B2
公开(公告)日：2022-03-01
申请号：US16539824
申请日：2019-08-13
申请人： Private Identity LLC
发明人： Scott Edward Streit
IPC分类号： G06K9/00 , G06K9/46 , G06F21/32 , G06N3/08 , H04L9/32 , G06F21/62 , H04L9/00 , G06N20/00 , G06N3/04 , G06V10/44 , G06V30/194
摘要： 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.

6. 发明授权

US11170084B2 Biometric authentication 有权
公开(公告)号：US11170084B2
公开(公告)日：2021-11-09
申请号：US16022101
申请日：2018-06-28
申请人： Private Identity LLC
发明人： Scott Edward Streit
IPC分类号： G06F21/32
摘要： 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.

7. 发明授权

US11138333B2 Systems and methods for privacy-enabled biometric processing 有权
公开(公告)号：US11138333B2
公开(公告)日：2021-10-05
申请号：US15914969
申请日：2018-03-07
申请人： Private Identity LLC
发明人： Scott Edward Streit
IPC分类号： G06F21/62 , H04L9/00 , G06F21/32 , G06N3/02 , H04L29/06 , G06F21/60 , G06K9/62
摘要： 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.

8. 发明申请

US20200351097A1 SYSTEMS AND METHODS FOR PRIVACY-ENABLED BIOMETRIC PROCESSING 审中-公开
公开(公告)号：US20200351097A1
公开(公告)日：2020-11-05
申请号：US16933428
申请日：2020-07-20
申请人： Private Identity LLC
发明人： Scott Edward Streit
IPC分类号： H04L9/32 , G06N3/08 , H04L9/00 , G06F21/32
摘要： 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.

9. 发明申请

US20230043127A1 SYSTEMS AND METHODS FOR PRIVACY-ENABLED BIOMETRIC PROCESSING 有权
公开(公告)号：US20230043127A1
公开(公告)日：2023-02-09
申请号：US17682081
申请日：2022-02-28
申请人： Private Identity LLC
发明人： Scott Edward Streit
IPC分类号： H04L9/32 , G06F21/32 , G06F21/62 , G06N3/08 , H04L9/00 , G06N20/00 , G06N3/04 , G06V10/44 , G06V30/194
摘要： 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.

10. 发明授权

US11502841B2 Systems and methods for privacy-enabled biometric processing 有权
公开(公告)号：US11502841B2
公开(公告)日：2022-11-15
申请号：US16573851
申请日：2019-09-17
申请人： Private Identity LLC
发明人： Scott Edward Streit
IPC分类号： H04L29/06 , H04L9/32 , G06N3/04 , G06F21/32 , G06N3/08
摘要： 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.

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