SensitiveNets: Learning Agnostic Representations with Application to Face Images

Understand

This work proposes a novel privacy-preserving neural network feature representation to suppress the sensitive information of a learned space while maintaining the utility of the data.

• The new international regulation for personal data protection forces data controllers to guarantee privacy and avoid discriminative hazards while managing sensitive data of users.
• In our approach, privacy and discrimination are related to each other.
• Instead of existing approaches aimed directly at fairness improvement, the proposed feature representation enforces the privacy of selected attributes.