Privacy Preserving Set Intersection Protocol Secure against Malicious Behaviors

Yingpeng Sang, Hong Shen
2007 Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2007)  
When datasets are distributed on different sources, finding out their intersection while preserving the privacy of the datasets is a widely required task. In this paper, we address the Privacy Preserving Set Intersection (PPSI) problem, in which each of the N parties learns no elements other than the intersection of their N private datasets. We propose an efficient protocol in the malicious model, where the adversary may control arbitrary number of parties and execute the protocol for its own
more » ... nefit. A related work in [12] has a correctness probability of ( N −1 N ) N (N is the size of the encryption scheme's plaintext space), a computation complexity of O(N 2 S 2 lgN ) (S is the size of each party's data set). Our PPSI protocol in the malicious model has a correctness probability of ( N −1 N ) N −1 , and achieves a computation cost of O(c 2 S 2 lgN ) (c is the number of malicious parties and c ≤ N − 1).
doi:10.1109/pdcat.2007.4420204 fatcat:4rxssl4oijcrxfs6ergcpvbhmq