Privacy Preserving Set Intersection Protocol Secure against Malicious Behaviors

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 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 complexity of O(N2 S2 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(c2 S2 lgN) (c is the number of malicious parties and c ≤ N − 1). Keywords : cryptographic protocol, privacy preservation, distributed dataset...

• Type: Conference
• Year: 2007
• Where: PDCAT
• Authors: Yingpeng Sang, Hong Shen
• Keywords: Correctness Probability, Distributed And Parallel Computing, Malicious Model, PDCAT 2007, Set Intersection