Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

TL;DR

This paper introduces RAP, an adversarial learning-based recommendation model that balances high-quality recommendations with privacy protection against private-attribute inference attacks.

Contribution

It is the first model to integrate private-attribute protection directly into the recommendation process using adversarial learning.

Findings

Preserves recommendation quality while protecting private attributes.

Effectively counters private-attribute inference attacks.

Outperforms baseline methods in privacy preservation.

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.