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

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... 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).