Please use this identifier to cite or link to this item: http://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/3311
Title: Privacy preserving network analysis of distributed social networks
Authors: Kukkala, V. B.
Saini, J. S.
Iyengar, S. R. S.
Keywords: Social network analysis
Secure multiparty computation
Centrality measures
Issue Date: 17-Dec-2021
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, 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 comparison, equality and modulo operations
URI: http://localhost:8080/xmlui/handle/123456789/3311
Appears in Collections:Year-2016

Files in This Item:
File Description SizeFormat 
Full Text.pdf270.53 kBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.