Encrypted Biometric Matching Using Homomorphic Feature Vectors
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Solution Overview
Problem
Conventional biometric systems face limitations in performing one-to-many searches on encrypted biometric data, lacking scalability and security, and are burdened by key management overheads, with existing approaches often compromising biometric data by searching in clear text.
Innovation Solution
Implementing a privacy-enabled biometric system using deep neural networks (DNNs) for encrypted feature vector processing, enabling one-to-many searches in polynomial time with one-way homomorphic encryption, ensuring secure operations without decrypting biometric data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional biometric systems search biometrics in clear text, then searching and matching can be performed, but security is compromised and key management overhead increases
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary that enables searching and matching operations to be performed on encrypted biometric data without decryption. The encrypted feature vectors serve as the intermediary representation that maintains security while enabling computational operations through polynomial approximation methods.
Solution Approach 2:
The system transforms biometric data into encrypted feature vectors using homomorphic encryption, changing the parameter state from plaintext to ciphertext. This parameter transformation enables the system to perform searching and matching operations on encrypted data while maintaining security, resolving the contradiction between security and operational capability.
2Reliability
If one-to-many searching is performed on encrypted biometrics, then security is maintained, but conventional approaches cannot perform the search efficiently
Solution Approach 1:
The patent replaces conventional mechanical searching methods with polynomial approximation techniques that operate on encrypted feature vectors. This substitution enables efficient one-to-many searching on encrypted biometric data by using mathematical approximations rather than exhaustive comparison methods.
Solution Approach 2:
The system changes the operational parameters by performing computations in the encrypted domain using homomorphic encryption properties. This parameter change enables efficient searching without decryption, maintaining security while improving productivity through optimized polynomial-based comparison algorithms.
3Reliability
If biometric data is encrypted for security, then security is improved, but conventional systems cannot perform matching operations on the encrypted data
Solution Approach 1:
Homomorphic encryption serves as the intermediary that enables matching operations on encrypted biometric data. The encrypted feature vectors allow the system to perform similarity comparisons and identification tasks without exposing the underlying biometric information, thus maintaining security while preserving matching capability.
Solution Approach 2:
The patent creates a universal system that can perform multiple functions (searching, matching, identification) on encrypted biometric data using the same homomorphic encryption framework. This multi-functional approach enables the system to maintain security while adapting to various biometric processing tasks.
Data Source
AI summary
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.