Compact Fuzzy Private Matching via Fully Homomorphic Encryption
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing fuzzy private matching protocols using partially homomorphic encryption schemes are insecure and inefficient, particularly in terms of communication complexity and computational performance, especially when dealing with imperfect data and the need for fuzzy matching of encrypted data.
Innovation Solution
The implementation of a Compact Fuzzy Private Matching protocol using a Fully Homomorphic Encryption scheme that allows both addition and multiplication operations, leveraging lattice-based asymmetric cryptographic primitives to create compact data structures, reducing transmission costs and improving computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If partially homomorphic encryption schemes are used for fuzzy private matching, then implementation simplicity is maintained, but security and efficiency deteriorate
Solution Approach 1:
The patent transitions from partially homomorphic encryption parameters to fully homomorphic encryption parameters, enabling both addition and multiplication operations on ciphertext. This parameter change in the encryption scheme allows secure fuzzy private matching while maintaining implementation feasibility through systematic protocol design.
2Reliability
If fully homomorphic encryption is implemented for fuzzy private matching, then security and functionality are improved, but computational complexity increases
Solution Approach 1:
The patent segments the fuzzy private matching protocol into distinct phases: encryption setup, homomorphic computation of similarity metrics, threshold comparison, and result decryption. This segmentation of the computational process manages complexity by organizing operations into modular, manageable stages while leveraging fully homomorphic encryption capabilities.
Solution Approach 2:
The patent introduces encrypted similarity metrics as an intermediary representation between the original data and the final matching result. This intermediary allows the system to perform fuzzy matching computations on encrypted data without requiring decryption, thereby maintaining security while managing computational complexity through efficient homomorphic operations.
3Device complexity
If traditional encryption schemes are used, then implementation simplicity is maintained, but privacy protection during data processing deteriorates
Solution Approach 1:
The patent enables continuous useful action by performing fuzzy matching computations directly on encrypted data throughout the entire processing pipeline. The fully homomorphic encryption scheme allows addition, multiplication, and threshold comparison operations to be executed continuously on ciphertext without interruption for decryption, maintaining both privacy protection and computational efficiency.
Data Source
AI summary
A method for data matching includes providing two sets of encrypted data elements by converting data elements to respective sets of vectors and encrypting each vector with a public key of a homomorphic encryption scheme. Each data element includes a sequence of characters drawn from an alphabet. For pairs of encrypted data elements, a comparison measure is computed between the sets of encrypted vectors. An obfuscated vector is generated for each encrypted data element in the first set, which renders the first encrypted data element indecipherable when the comparison measure does not meet a threshold for at least one of the pairs of data encrypted elements comprising that encrypted data element. The obfuscated vectors can be decrypted with a private key, allowing data elements in the first set to be deciphered if the comparison measure meets the threshold for at least one of the data elements in the second set.