When differential privacy meets randomized perturbation

a hybrid approach for privacy-preserving recommender system

Xiao Liu, An Liu*, Xiangliang Zhang, Zhixu Li, Guanfeng Liu, Lei Zhao, Xiaofang Zhou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

15 Citations (Scopus)

Abstract

Privacy risks of recommender systems have caused increasing attention. Users' private data is often collected by probably untrusted recommender system in order to provide high-quality recommendation. Meanwhile, malicious attackers may utilize recommendation results to make inferences about other users' private data. Existing approaches focus either on keeping users' private data protected during recommendation computation or on preventing the inference of any single user's data from the recommendation result. However, none is designed for both hiding users' private data and preventing privacy inference. To achieve this goal, we propose in this paper a hybrid approach for privacy-preserving recommender systems by combining differential privacy (DP) with randomized perturbation (RP). We theoretically show the noise added by RP has limited effect on recommendation accuracy and the noise added by DP can be well controlled based on the sensitivity analysis of functions on the perturbed data. Extensive experiments on three large-scale real world datasets show that the hybrid approach generally provides more privacy protection with acceptable recommendation accuracy loss, and surprisingly sometimes achieves better privacy without sacrificing accuracy, thus validating its feasibility in practice.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications
Subtitle of host publication22nd International Conference, DASFAA 2017, Proceedings, Part I
EditorsSelçuk Candan, Lei Chen, Torben Bach Pedersen, Lijun Chang, Wen Hua
PublisherSpringer, Springer Nature
Pages576-591
Number of pages16
ISBN (Electronic)9783319557533
ISBN (Print)9783319557526
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event22nd International Conference on Database Systems for Advanced Applications (DASFAA) - Suzhou
Duration: 27 Mar 201730 Mar 2017

Publication series

NameLecture Notes in Computer Science
PublisherSPRINGER INTERNATIONAL PUBLISHING AG
Volume10177
ISSN (Print)0302-9743

Conference

Conference22nd International Conference on Database Systems for Advanced Applications (DASFAA)
CitySuzhou
Period27/03/1730/03/17

Keywords

  • Recommender systems
  • Privacy-preserving
  • Differential privacy
  • Randomized perturbation

Cite this

Liu, X., Liu, A., Zhang, X., Li, Z., Liu, G., Zhao, L., & Zhou, X. (2017). When differential privacy meets randomized perturbation: a hybrid approach for privacy-preserving recommender system. In S. Candan, L. Chen, T. B. Pedersen, L. Chang, & W. Hua (Eds.), Database Systems for Advanced Applications: 22nd International Conference, DASFAA 2017, Proceedings, Part I (pp. 576-591). (Lecture Notes in Computer Science; Vol. 10177). Springer, Springer Nature. https://doi.org/10.1007/978-3-319-55753-3_36