Research Information System
10th International Workshop on Data Privacy Management, and Security Assurance, DPM 2015 and 4th International Workshop on Quantitative Aspects in Security Assurance, QASA 2015, Vienna, Austria, 21 - 22 September 2015, vol.9481, pp.199-214, (Full Text)
© Springer International Publishing Switzerland 2016.Collaborative filtering systems provide recommendations for their users. Privacy is not a primary concern in these systems; however, it is an important element for the true user participation. Privacy-preserving collaborative filtering techniques aim to offer privacy measures without neglecting the recommendation accuracy. In general, these systems rely on the data residing on a central server. Studies show that privacy is not protected as much as believed. On the other hand, many e-companies emerge with the advent of the Internet, and these companies might collaborate to offer better recommendations by sharing their data. Thus, partitioned data-based privacy-persevering collaborative filtering schemes have been proposed. In this study, we explore possible attacks on two-party binary privacy-preserving collaborative filtering schemes and evaluate them with respect to privacy performance.