PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation

TL;DR

This paper introduces PrivNet, a transfer learning approach for recommendation systems that enhances target performance while safeguarding source domain privacy through adversarial training to prevent private information leakage.

Contribution

It proposes a novel privacy-aware neural representation learning method that balances transfer effectiveness with source privacy protection in recommendation systems.

Findings

PrivNet effectively disentangles transfer knowledge from private information.

The model demonstrates robustness against privacy attacks.

Experimental results show improved privacy preservation without sacrificing recommendation accuracy.

Abstract

Transfer learning is an effective technique to improve a target recommender system with the knowledge from a source domain. Existing research focuses on the recommendation performance of the target domain while ignores the privacy leakage of the source domain. The transferred knowledge, however, may unintendedly leak private information of the source domain. For example, an attacker can accurately infer user demographics from their historical purchase provided by a source domain data owner. This paper addresses the above privacy-preserving issue by learning a privacy-aware neural representation by improving target performance while protecting source privacy. The key idea is to simulate the attacks during the training for protecting unseen users' privacy in the future, modeled by an adversarial game, so that the transfer learning model becomes robust to attacks. Experiments show that the proposed PrivNet model can successfully disentangle the knowledge benefitting the transfer from leaking the privacy.