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http://dx.doi.org/10.3837/tiis.2019.09.008

Deriving ratings from a private P2P collaborative scheme  

Okkalioglu, Murat (Computer Engineering Department, Yalova University)
Kaleli, Cihan (Computer Engineering Department, Eskisehir Technical University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.9, 2019 , pp. 4463-4483 More about this Journal
Abstract
Privacy-preserving collaborative filtering schemes take privacy concerns into its primary consideration without neglecting the prediction accuracy. Different schemes are proposed that are built upon different data partitioning scenarios such as a central server, two-, multi-party or peer-to-peer network. These data partitioning scenarios have been investigated in terms of claimed privacy promises, recently. However, to the best of our knowledge, any peer-to-peer privacy-preserving scheme lacks such study that scrutinizes privacy promises. In this paper, we apply three different attack techniques by utilizing auxiliary information to derive private ratings of peers and conduct experiments by varying privacy protection parameters to evaluate to what extent peers' data can be reconstructed.
Keywords
Privacy; data reconstruction; auxiliary information; peer-to-peer; collaborative filtering;
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