Concealed Data Matching Using Remainder Vectors for Biometric Authentication
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Solution Overview
Problem
Conventional key binding schemes by lattice element addition in biometric authentication are limited in supporting various standards for different types of biometric information, leading to reduced authentication accuracy when forced standards are applied.
Innovation Solution
A concealed data matching method that registers and matches data using concealed vectors generated by combining biometric and key data with random numbers and row vectors of a determination matrix, calculating a remainder vector to determine similarity and extract key data based on inter-vector distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single standard is used for all biometric information types in conventional key binding schemes, then device complexity is reduced, but authentication accuracy deteriorates
Solution Approach 1:
The patent changes the parameter of similarity calculation from fixed component differences to multiple distance metrics (Hamming distance, square norm distance, etc.) that can be selected based on biometric information type. This allows the system to adapt parameters to different authentication scenarios, improving accuracy without significantly increasing complexity.
Solution Approach 2:
The patent introduces dynamic selection of similarity standards based on the type of biometric information being authenticated. The system can dynamically switch between different distance calculation methods (Hamming distance for binary data, square norm distance for continuous data) rather than using a static, single standard approach.
2Measurement precision
If multiple templates are used to support various biometric information types, then authentication accuracy is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal key binding scheme that can handle multiple biometric information types through a unified framework. The determination matrix and lattice element addition mechanism work universally across different data types, while allowing flexible selection of similarity metrics. This eliminates the need for separate templates for each biometric type.
Solution Approach 2:
Instead of maintaining multiple templates with different structures, the patent changes the parameter of similarity calculation to accommodate different biometric types. The system uses a single template structure but adapts the distance metric parameter based on the biometric information type, reducing complexity while maintaining accuracy.
3Device complexity
If authentication threshold is fixed in conventional schemes, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent makes the authentication threshold dynamic by allowing it to be adjusted based on security requirements and biometric information types. The threshold is not fixed but can be modified to suit different authentication scenarios, enhancing adaptability while maintaining system simplicity through a unified threshold management mechanism.
Solution Approach 2:
The patent changes the threshold from a fixed parameter to an adjustable parameter that can be optimized for different security levels and biometric types. This allows the system to adapt to various authentication requirements without increasing structural complexity, as the threshold can be configured based on specific application needs.
Data Source
AI summary
A concealed data matching method for a computer including: registering a first concealed vector obtained by concealing registered data and key data based on a first random number and a linear combination of row vectors of a determination matrix; acquiring a second concealed vector; calculating a remainder vector indicating a remainder obtained by dividing the difference between the first concealed vector and the second concealed vector; determining the similarity between the registered data and the matching data based on the remainder vector; extracting the key data from the remainder vector if it is determined they are similar; calculating an inter-vector distance between the registered data and the matching data; and determining the similarity between the registered data and the matching data based on the magnitude of the inter-vector distance.