Cryptology ePrint Archive: Report 2016/427

Privacy Preserving Network Analysis of Distributed Social Networks

Varsha Bhat Kukkala and Jaspal Singh Saini and S.R.S. Iyengar

Abstract: Social network analysis as a technique has been applied to a diverse set of fields, including, organizational behavior, sociology, economics and biology. However, for sensitive networks such as hate networks, trust networks and sexual networks, these techniques have been sparsely used. This is majorly attributed to the unavailability of network data. Anonymization is the most commonly used technique for performing privacy preserving network analysis. The process involves the presence of a trusted third party, who is aware of the complete network, and releases a sanitized version of it. In this paper, we propose an alternative, in which, the desired analysis can be performed by the parties who distributedly hold the network, such that : (a) no central third party is required; (b) the topology of the underlying network is kept hidden. We design multiparty protocols for securely performing few of the commonly studied social network analysis algorithms. The current paper addresses a secure implementation of the most commonly used network analysis measures, which include degree distribution, closeness centrality, PageRank algorithm and K-shell decomposition algorithm. The designed protocols are proven to be secure in the presence of an arithmetic black-box extended with operations like comparison and modulo.

Category / Keywords: social network analysis, secure multiparty computation, centrality measures

Original Publication (with minor differences): International Conference on Information Systems Security 2016

Date: received 30 Apr 2016, last revised 30 Sep 2016

Contact author: varsha bhat at iitrpr ac in

Available format(s): PDF | BibTeX Citation

Note: Minor changes included.

Version: 20160930:063607 (All versions of this report)

Short URL: ia.cr/2016/427

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