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Privacy Preserving Set Intersection Protocol Secure against Malicious Behaviors

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2 Author(s)

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 ( v;1)ldquo (f is the size of the encryption scheme's plaintext space), a computation complexity of' 0(N2 S2lgf) (S is the size of each party's data set). Our PPSI protocol in the malicious model has a correctness probability iquest/C a/-1)JV~1 plusmnmiddotd achieves a computation cost of 0{c2S2lgM) (c is the number of malicious parties and c < N eurordquo I).

Published in:

Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2007)

Date of Conference:

3-6 Dec. 2007