Biometric Feature Extraction via Fragmented Multi-Node Computing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Biometric feature vectors are calculated and stored by a single device, posing a risk of leakage and compromising data security.
Innovation Solution
A biometric feature extraction method and apparatus that utilizes a multi-party secure computing system, where a biometric feature extraction model is fragmented into multiple model fragments deployed across different nodes, with each node extracting part of the biometric feature vector through joint processing with other nodes, ensuring that no single device calculates the entire vector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If biometric feature vectors are calculated and stored by a single device, then processing efficiency is improved, but data security deteriorates due to leakage risk
Solution Approach 1:
The patent divides the biometric feature extraction model into multiple model fragments and distributes them across different nodes in a multi-party secure computing system. Each node processes only a portion of the biometric information through its local model fragment, and the complete feature vector is reconstructed through secure aggregation of results from all nodes. This segmentation prevents any single device from possessing or leaking the complete biometric feature vector, thereby resolving the contradiction between processing efficiency and data security.
2Reliability
If biometric feature extraction model is fragmented and distributed across multiple nodes, then data security is improved, but system complexity increases
Solution Approach 1:
The patent introduces a multi-party secure computing system as an intermediary framework that manages the complexity of distributed model fragmentation. This system provides standardized interfaces for model fragment distribution, coordinated processing, and secure result aggregation. By encapsulating the complexity within this intermediary framework, the system achieves enhanced data security through distribution while maintaining manageable system complexity through standardized communication protocols and centralized coordination mechanisms.
3Reliability
If biometric feature extraction model is fragmented and distributed, then data security is improved, but processing efficiency deteriorates due to distributed computation overhead
Solution Approach 1:
The patent merges the computational power of multiple nodes in the multi-party secure computing system to process biometric information in parallel. Each node simultaneously processes its assigned model fragment on its local data portion, and the results are securely aggregated to produce the complete feature vector. This merging of distributed computational resources maintains processing efficiency comparable to centralized systems while achieving the security benefits of distribution, as the parallel processing eliminates the sequential overhead that would otherwise reduce efficiency.
Data Source
AI summary
The embodiments of the present application relate to the technical field of artificial intelligence. Provided are a biological feature extraction method and apparatus. Fragmentation processing is performed on a biological feature extraction model to obtain a plurality of feature extraction model fragments, and the plurality of feature extraction model fragments are respectively deployed in different nodes, thereby ensuring that model parameters are not leaked. Moreover, fragmentation processing is performed on target biological information to obtain a plurality of biological information fragments; then, the plurality of biological information fragments are distributed to different nodes; and each node jointly performs feature extraction on the biological information fragments on the basis of its own locally deployed feature extraction model fragment and the feature extraction model fragments deployed in the other nodes, so as to obtain a biological feature vector fragment, such that the problem of a whole biological feature vector being obtained by means of calculation by a single device and being stored in a single environment is solved, thereby improving the security of biological feature extraction. In addition, the present application provides a universal calculation solution, which is applicable to various scenarios and has strong universality.