Research On The Problem Of Secure Multi-party Data Comparison

With the rapid development of internet,internet of things,big data,people can obtain,analyze and utilize data easier and easier.In information society,it is necessary to perform cooperative computation on the data owned by different identities to efficiently solve problems in many cases.The cooperative computation can easily disclose the privacy of the data.Therefore,it is an important problem of information society how to share data to perform cooperative computation while properly preserving the privacy.Many privacy preserving problems can be solved by secure multiparty computation,therefore secure multiparty computation has become the key technology of privacy preserving in the information society.Secure multiparty computation enables participants to make full use of their own private data to perform cooperative computation while preserving the privacy of their data to benefit the development of society,economics and science and technology.It has become a main tool to perform cooperative computation.Secure multiparty data comparison problem is one of the hot topics of secure multiparty computation,and there are still many sub-problems that need to be solved.On the one hand,the security of the existing solutions conflict with their efficiency,we need secure and efficient solutions.On the other hand,the existing solutions are applicable to scenarios where the data range is known and the range is not large,and they cannot work in some real cases.It is necessary to investigate more universal solutions.Finally,there are many unsolved sub-problems in this field and it is necessary to investigate solutions to these sub-problems to meet people's demand for private cooperative computation.Based on the existing research,we make an in-depth study on secure multiparty data comparison.The main contributions are as follows:1.We study the private maximum and minimum computation problem.We propose a new encoding method for private data,and combine the encoding method with the Paillier encryption algorithm to design an efficient protocol to solve this problem.Then we design a special encoding method to ensure that elliptic curve encryption algorithm has additive homomorphism for plaintext.We further use threshold decryption elliptic curve encryption algorithm to design a protocol to privately compute the maximum and minimum of private data.Finally,based on the same encoding method we design a protocol to privately determine whether a private number is in a private interval.We prove the correctness and security of protocols,test their efficiency,and compare them with the existing protocols.2.We study the secure multiparty data equality test problem.For private data that without known range,we propose a new method for multiple parties to test whether their private data are equal,and combine with the Paillier encryption algorithm to design a multi-data equality test protocol.Then we use Godel encoding scheme to design a more secure protocol for the multi-data equality test problem.We analyze the correctness,prove the security,and test the efficiency of protocols.The experiments show that our protocols outperform the existing protocols.3.Based on secure multiparty data equality test scheme,we solve the problems of multiple strings equality test,private hamming distance computation and multiparty data equality test in the malicious model,and give specific protocols.