When datasets are distributed on different sources, find- ing 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 adver- sary may control arbitrary number of parties and execute the protocol for its own benefit. 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 com- plexity of O(N 2S2lg N ) (S is the size of each party's data set). Our PPSI protocol in the malicious model has a cor- rectness probability of ( N -1 N )N-1, and achieves a compu- tation cost of O(c2S2lg N ) (c is the number of malicious parties and c N - 1). Keywords : cryptographic protocol, privacy preserva- tion, distributed datasets, set intersection, zero-knowledge proof.