刊物主题:Management of Computing and Information Systems Data Structures, Cryptology and Information Theory
出版者:Springer Netherlands
ISSN:1573-7659
卷排序:19
文摘
Privacy-preserving collaborative filtering algorithms are successful approaches. However, they are susceptible to shilling attacks. Recent research has increasingly focused on collaborative filtering to protect against both privacy and shilling attacks. Malicious users may add fake profiles to manipulate the output of privacy-preserving collaborative filtering systems, which reduces the accuracy of these systems. Thus, it is imperative to detect fake profiles for overall success. Many methods have been developed for detecting attack profiles to keep them outside of the system. However, these techniques have all been established for non-private collaborative filtering schemes. The detection of shilling attacks in privacy-preserving recommendation systems has not been deeply examined. In this study, we examine the detection of shilling attacks in privacy-preserving collaborative filtering systems. We utilize four attack-detection methods to filter out fake profiles produced by six well-known shilling attacks on perturbed data. We evaluate these detection methods with respect to their ability to identify bogus profiles. Real data-based experiments are performed. Empirical outcomes demonstrate that some of the detection methods are very successful at filtering out fake profiles in privacy-preserving collaborating filtering schemes.
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