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Privacy-Preserving SimRank over Distributed Information Network

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4 Author(s)
Yu-Wei Chu ; Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan ; Chih-Hua Tai ; Ming-Syan Chen ; Yu, P.S.

Information network analysis has drawn a lot attention in recent years. Among all the aspects of network analysis, similarity measure of nodes has been shown useful in many applications, such as clustering, link prediction and community identification, to name a few. As linkage data in a large network is inherently sparse, it is noted that collecting more data can improve the quality of similarity measure. This gives different parties a motivation to cooperate. In this paper, we address the problem of link-based similarity measure of nodes in an information network distributed over different parties. Concerning the data privacy, we propose a privacy-preserving Sim Rank protocol based on fully-homomorphic encryption to provide cryptographic protection for the links.

Published in:

Data Mining (ICDM), 2012 IEEE 12th International Conference on

Date of Conference:

10-13 Dec. 2012