Privacy-preserving Hamming distance Protocol and Its Applications

Hamming distance is one of the core problems in similarity calculation. It is a new problem in cryptography to calculate Hamming distance between two objects on the premise of privacy protection. It has important theoretical significance and application value in graph recognition, machine learning, artificial intelligence, bioinformatics and other aspects which are related to data privacy protection. In this paper, a new coding method is designed, which encodes the information of both sides to be calculated, transforms the problem into the one of finding the number of the same numbers between the two sets. Combined with the idea of XOR, we also solves the privacy protection problem of Hamming distance calculation with the help of homomorphic encryption algorithm. The solution in this paper is universal. The simulation results show that the protocol in this paper is secure. The analysis shows that the protocol can calculate the Hamming distance between two objects efficiently and safely.