Systems and Methods for Privacy-Enabled Biometric Processing
Summary
The USPTO published patent application US20260100842A1 filed by Private Identity LLC on May 20, 2025, covering systems and methods for privacy-enabled biometric processing. The invention derives encrypted feature vectors from biometric data and uses deep neural networks to authenticate users while preserving privacy. Homomorphic encryption enables authentication comparisons without decrypting the underlying biometric data.
What changed
The USPTO published Private Identity LLC's patent application US20260100842A1 for privacy-enabled biometric authentication systems. The technology converts biometric and behavioral data into encrypted feature vectors that can be compared using deep neural networks without exposing the original data. The system employs homomorphic encryption to enable authentication computations on encrypted data, with liveness detection to prevent spoofing. Original biometric data is discarded after vector generation.
For parties developing or using biometric authentication systems, this published application establishes a priority date of May 20, 2025 and should be considered as potential prior art. Technology companies in authentication, identity verification, or privacy-preserving computation may want to review the claims for potential licensing considerations or design-around opportunities.
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Archived snapshot
Apr 12, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
SYSTEMS AND METHODS FOR PRIVACY-ENABLED BIOMETRIC PROCESSING
Application US20260100842A1 Kind: A1 Apr 09, 2026
Assignee
Private Identity LLC
Inventors
Scott Edward Streit
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.
CPC Classifications
H04L 9/3231 G06F 21/32 G06N 3/045 G06N 3/08 G06F 2221/2133
Filing Date
2025-05-20
Application No.
19213476
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