期刊
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
卷 7, 期 5, 页码 1272-1290出版社
KSII-KOR SOC INTERNET INFORMATION
DOI: 10.3837/tiis.2013.05.019
关键词
Shilling; privacy; robustness; recommendation; profile injection; collaborative filtering
资金
- TUBITAK [111E218]
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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