On the Privacy of Horizontally Partitioned Binary Data-Based Privacy-Preserving Collaborative Filtering

Okkalioglu M., KOÇ M., Polat H.

10th Data Privacy Management International Workshop (DPM) / 4th International Workshop in Quantitative Aspects in Security Assurance (QASA), Vienna, Austria, 21 - 22 September 2015, vol.9481, pp.199-214 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 9481
  • Doi Number: 10.1007/978-3-319-29883-2_13
  • City: Vienna
  • Country: Austria
  • Page Numbers: pp.199-214
  • Keywords: Privacy, Collaborative filtering, Binary data, Attack scenarios, RANDOMIZED-RESPONSE, RECOMMENDATIONS, INFORMATION
  • Anadolu University Affiliated: Yes


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.