Privacy-Aware Detection of Shilling Profiles on Arbitrarily Distributed Recommender Systems

Abstract

Due to the mutual advantage of small-scale online service providers, they need to collaborate to deliver recommendations based on arbitrarily distributed preference data without jeopardizing their confidentiality. Besides privacy issues, parties also have concerns regarding the vulnerability against recommendation manipulation attempts, referred to as shilling attacks. Although there are methods for detecting these injected malicious profiles in central server-based configurations, they are not readily suitable for employing arbitrarily distributed data. In this paper, we present a novel classification-based shilling attack detection protocol enabling the recognition of malicious profiles in arbitrarily distributed configurations without compromising the privacy of collaborating parties. The analysis of the proposed protocol regarding confidentiality of parties reveals that the process is bound to collaboration by design, which does not allow parties to achieve detection by themselves. Furthermore, empirical evaluations using real-world preference data demonstrate that the protocol can achieve significantly high detection rates facilitating privacy-aware data collaboration.