SensitiveNets: Learning Agnostic Representations with Application to Face Images
This addresses privacy and discrimination risks in personal data management, particularly for face images, offering a novel approach that enforces privacy to indirectly achieve fairness, though it is incremental in combining adversarial methods with privacy goals.
The authors tackled the problem of suppressing sensitive information in neural network feature representations while maintaining data utility, achieving improved privacy and equality of opportunity with competitive performance across tasks like identity, attractiveness, and smiling on three benchmarks.
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. This way fairness is not the objective, but the result of a privacy-preserving learning method. This approach guarantees that sensitive information cannot be exploited by any agent who process the output of the model, ensuring both privacy and equality of opportunity. Our method is based on an adversarial regularizer that introduces a sensitive information removal function in the learning objective. The method is evaluated on three different primary tasks (identity, attractiveness, and smiling) and three publicly available benchmarks. In addition, we present a new face annotation dataset with balanced distribution between genders and ethnic origins. The experiments demonstrate that it is possible to improve the privacy and equality of opportunity while retaining competitive performance independently of the task.