Making Users Indistinguishable: Attribute-wise Unlearning in Recommender
  Systems
Yuyuan Li Chaochao Chen Xiaolin Zheng Yizhao Zhang Zhongxuan Han Dan Meng Jun Wang
With the growing privacy concerns in recommender systems, recommendation
unlearning, i.e., forgetting the impact of specific learned targets, is getting
increasing attention. Existing studies predominantly use training data, i.e.,
model inputs, as the unlearning target. However, we find that attackers can
extract private information, i.e., gender, race, and age, from a trained model
even if it has not been explicitly encountered during training. We name this
unseen information as attribute and treat it as the unlearning target. To
protect the sensitive attribute of users, Attribute Unlearning (AU) aims to
degrade attacking performance and make target attributes indistinguishable. In
this paper, we focus on a strict but practical setting of AU, namely
Post-Training Attribute Unlearning (PoT-AU), where unlearning can only be
performed after the training of the recommendation model is completed. To
address the PoT-AU problem in recommender systems, we design a two-component
loss function that consists of i) distinguishability loss: making attribute
labels indistinguishable from attackers, and ii) regularization loss:
preventing drastic changes in the model that result in a negative impact on
recommendation performance. Specifically, we investigate two types of
distinguishability measurements, i.e., user-to-user and
distribution-to-distribution. We use the stochastic gradient descent algorithm
to optimize our proposed loss. Extensive experiments on three real-world
datasets demonstrate the effectiveness of our proposed methods.