RAP: a light-weight privacy-preserving framework for recommender systems

Miao Hu, Di Wu, Run Wu, Zhengkai Shi, Min Chen, Yipeng Zhou

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In today's Internet, recommender systems play an indispensable role in helping users discover items of interests, such as products, books, movies and so on. However, a higher recommendation accuracy is commonly at the cost of more disclosure of user privacy. Thus, a wider adoption of recommender systems poses significant security and privacy concerns to users. In this paper, we propose a light-weight privacy-preserving framework called RAP for recommender systems, which can protect user privacy while still ensuring a high recommendation accuracy. Instead of directly sending users' private ratings to the recommender, users first conduct a local perturbation operation on private ratings, and then send the perturbed ratings to the recommender. The recommender can run recommendation algorithms directly over the perturbed ratings and return the results to users. Different from crypto-based methods, our perturbation and de-perturbation methods are linear operations. Thus, RAP is light-weight and highly efficient in privacy protection. To be more rigorous, we formally prove that the order of recommendation accuracy will not decrease when our RAP framework is applied to any MF (Matrix Factorization)-based recommender systems. We also derive the closed-form expression for the degree of privacy preservation of our framework and conduct extensive evaluations using real-world datasets.

Original languageEnglish
JournalIEEE Transactions on Services Computing
Early online date9 Mar 2021
DOIs
Publication statusE-pub ahead of print - 9 Mar 2021

Bibliographical note

Publisher Copyright:
IEEE

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Differential privacy
  • Encryption
  • Internet
  • Motion pictures
  • Perturbation methods
  • privacy
  • Privacy
  • rating perturbation
  • recommendation
  • Recommender systems

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