Privacy-aware recommendation with private-attribute protection using adversarial learning

Ghazaleh Beigi, Ahmadreza Mosallanezhad, Ruocheng Guo, Hamidreza Alvari, Alexander Nou, Huan Liu

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

Abstract

Recommendation is one of the critical applications that helps users find information relevant to their interests. However, a malicious attacker can infer users’ private information via recommendations. Prior work obfuscates user-item data before sharing it with recommendation system. This approach does not explicitly address the quality of recommendation while performing data obfuscation. Moreover, it cannot protect users against private-attribute inference attacks based on recommendations. This work is the first attempt to build a Recommendation with Attribute Protection (RAP) model which simultaneously recommends relevant items and counters private-attribute inference attacks. The key idea of our approach is to formulate this problem as an adversarial learning problem with two main components: the private attribute inference attacker, and the Bayesian personalized recommender. The attacker seeks to infer users’ private-attribute information according to their items list and recommendations. The recommender aims to extract users’ interests while employing the attacker to regularize the recommendation process. Experiments show that the proposed model both preserves the quality of recommendation service and protects users against private-attribute inference attacks.

Original languageEnglish (US)
Title of host publicationWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages34-42
Number of pages9
ISBN (Electronic)9781450368223
DOIs
StatePublished - Jan 20 2020
Event13th ACM International Conference on Web Search and Data Mining, WSDM 2020 - Houston, United States
Duration: Feb 3 2020Feb 7 2020

Publication series

NameWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining

Conference

Conference13th ACM International Conference on Web Search and Data Mining, WSDM 2020
CountryUnited States
CityHouston
Period2/3/202/7/20

Keywords

  • Adversarial learning
  • Privacy
  • Privacy-aware recommendation
  • Private-attribute protection
  • Utility

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Computer Science Applications

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  • Cite this

    Beigi, G., Mosallanezhad, A., Guo, R., Alvari, H., Nou, A., & Liu, H. (2020). Privacy-aware recommendation with private-attribute protection using adversarial learning. In WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining (pp. 34-42). (WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining). Association for Computing Machinery, Inc. https://doi.org/10.1145/3336191.3371832