• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Shilling Attacks Against Memory-Based Privacy-Preserving Recommendation Algorithms

Vol. 7, No.5, May 31, 2013
10.3837/tiis.2013.05.019, Download Paper (Free):

Abstract

Privacy-preserving collaborative filtering schemes are becoming increasingly popular because they handle the information overload problem without jeopardizing privacy. However, they may be susceptible to shilling or profile injection attacks, similar to traditional recommender systems without privacy measures. Although researchers have proposed various privacy-preserving recommendation frameworks, it has not been shown that such schemes are resistant to profile injection attacks. In this study, we investigate two memory-based privacy-preserving collaborative filtering algorithms and analyze their robustness against several shilling attack strategies. We first design and apply formerly proposed shilling attack techniques to privately collected databases. We analyze their effectiveness in manipulating predicted recommendations by experimenting on real data-based benchmark data sets. We show that it is still possible to manipulate the predictions significantly on databases consisting of masked preferences even though a few of the attack strategies are not effective in a privacy-preserving environment.


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Cite this article

[IEEE Style]
Ihsan Gunes, Alper Bilge and Huseyin Polat, "Shilling Attacks Against Memory-Based Privacy-Preserving Recommendation Algorithms," KSII Transactions on Internet and Information Systems, vol. 7, no. 5, pp. 1272-1290, 2013. DOI: 10.3837/tiis.2013.05.019

[ACM Style]
Gunes, I., Bilge, A., and Polat, H. 2013. Shilling Attacks Against Memory-Based Privacy-Preserving Recommendation Algorithms. KSII Transactions on Internet and Information Systems, 7, 5, (2013), 1272-1290. DOI: 10.3837/tiis.2013.05.019