The secure judgment of graphic similarity against malicious adversaries and its applications - Xin Liu; Yang Xu; Dan Luo; Gang Xu; Neal Xiong; Xiu-Bo Chen (Scientific Reports, 2023)
Abstract With the advent of the era of big data, privacy computing analyzes and calculates data on the premise of protecting data privacy, to achieve data ‘available and invisible’. As an important branch of secure multi-party computation, the geometric problem can solve practical problems in the military, national defense, finance, life, and other fields, and has important research significance. In this paper, we study the similarity problem of geometric graphics. First, this paper proposes the adjacency matrix vector coding method of isomorphic graphics, and use the Paillier variant encryption cryptography to solve the problem of isomorphic graphics confidentiality under the semi-honest model. Using cryptography tools such as elliptic curve cryptosystem, zero-knowledge proof, and cut-choose method, this paper designs a graphic similarity security decision protocol that can resist malicious adversary attacks. The analysis shows that the protocol has high computational efficiency and has wide application value in terrain matching, mechanical parts, biomolecules, face recognition, and other fields.