PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation
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by
Guangneng Hu, Qiang Yang
2020
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.
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