Secure multi-party protocols for item-based collaborative filtering

Erez Shmueli, Tamir Tassa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Recommender systems have become extremely common in recent years, and are utilized in a variety of domains such as movies, music, news, products, restaurants, etc. While a typical recommender system bases its recommendations solely on users' preference data collected by the system itself, the quality of recommendations can significantly be improved if several recommender systems (or vendors) share their data. However, such data sharing poses significant privacy and security challenges, both to the vendors and the users. In this paper we propose secure protocols for distributed item-based Collaborative Filtering. Our protocols allow to compute both the predicted ratings of items and their predicted rankings, without compromising privacy nor predictions' accuracy. Unlike previous solutions in which the secure protocols are executed solely by the vendors, our protocols assume the existence of a mediator that performs intermediate computations on encrypted data supplied by the vendors. Such a mediated is advantageous over the non-mediated one since it enables each vendor to communicate solely with the mediator. This yields reduced communication costs and it allows each vendor to issue recommendations to its clients without being dependent on the availability and willingness of the other vendors to collaborate.

Original languageEnglish
Title of host publicationRecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Number of pages9
ISBN (Electronic)9781450346528
StatePublished - 27 Aug 2017
Event11th ACM Conference on Recommender Systems, RecSys 2017 - Como, Italy
Duration: 27 Aug 201731 Aug 2017

Publication series

NameRecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems


Conference11th ACM Conference on Recommender Systems, RecSys 2017

Bibliographical note

Publisher Copyright:
© 2017 ACM.


  • Distributed computing
  • Item-based collaborative filtering
  • Privacy


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