Privacy-preserving Collaborative Filtering by Distributed Mediation

Tamir Tassa, Alon Ben Horin

Research output: Contribution to journalArticlepeer-review


Recommender systems have become very influential in our everyday decision making, e.g., helping us choose a movie from a content platform, or offering us suitable products on e-commerce websites. While most vendors who utilize recommender systems rely exclusively on training data consisting of past transactions that took place through them, it would be beneficial to base recommendations on the rating data of more than one vendor. However, enlarging the training data by means of sharing information between different vendors may jeopardize the privacy of users. We devise here secure multi-party protocols that enable the practice of Collaborative Filtering (CF) in a manner that preserves the privacy of the vendors and users. Shmueli and Tassa [38] introduced privacy-preserving protocols of CF that involved a mediator; namely, an external entity that assists in performing the computations. They demonstrated the significant advantages of mediation in that context. We take here the mediation approach into the next level by using several independent mediators. Such distributed mediation maintains all of the advantages that were identified by Shmueli and Tassa, and offers additional ones, in comparison with the single-mediator protocols: stronger security and dramatically shorter runtimes. In addition, while all prior art assumed limited and unrealistic settings, in which each user can purchase any given item through only one vendor, we consider here a general and more realistic setting, which encompasses all previously considered settings, where users can choose between different competing vendors. We demonstrate the appealing performance of our protocols through extensive experimentation.

Original languageEnglish
Article number3542950
JournalACM Transactions on Intelligent Systems and Technology
Issue number6
StatePublished - 22 Sep 2022
Event15th ACM Conference on Recommender Systems, RecSys 2021 - Virtual, Online
Duration: 27 Sep 2021 → …

Bibliographical note

Publisher Copyright:
© 2022 Association for Computing Machinery.


  • Collaborative Filtering
  • Recommender systems
  • distributed computing
  • privacy
  • the mediated model


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