19,960 research outputs found
Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations
Latent factor models for recommender systems represent users and items as low
dimensional vectors. Privacy risks of such systems have previously been studied
mostly in the context of recovery of personal information in the form of usage
records from the training data. However, the user representations themselves
may be used together with external data to recover private user information
such as gender and age. In this paper we show that user vectors calculated by a
common recommender system can be exploited in this way. We propose the
privacy-adversarial framework to eliminate such leakage of private information,
and study the trade-off between recommender performance and leakage both
theoretically and empirically using a benchmark dataset. An advantage of the
proposed method is that it also helps guarantee fairness of results, since all
implicit knowledge of a set of attributes is scrubbed from the representations
used by the model, and thus can't enter into the decision making. We discuss
further applications of this method towards the generation of deeper and more
insightful recommendations.Comment: International Conference on Pattern Recognition and Method
VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition
Reliable facial expression recognition plays a critical role in human-machine
interactions. However, most of the facial expression analysis methodologies
proposed to date pay little or no attention to the protection of a user's
privacy. In this paper, we propose a Privacy-Preserving Representation-Learning
Variational Generative Adversarial Network (PPRL-VGAN) to learn an image
representation that is explicitly disentangled from the identity information.
At the same time, this representation is discriminative from the standpoint of
facial expression recognition and generative as it allows expression-equivalent
face image synthesis. We evaluate the proposed model on two public datasets
under various threat scenarios. Quantitative and qualitative results
demonstrate that our approach strikes a balance between the preservation of
privacy and data utility. We further demonstrate that our model can be
effectively applied to other tasks such as expression morphing and image
completion
- …