SensitiveNets: Learning Agnostic Representations with Application to Face Images

TL;DR

SensitiveNets introduces a privacy-preserving neural network approach that suppresses sensitive information in face representations, ensuring privacy and fairness without sacrificing task performance, validated across multiple benchmarks.

Contribution

The paper presents a novel adversarial regularizer-based method for learning face representations that inherently protect sensitive attributes, promoting privacy and fairness.

Findings

Effective suppression of sensitive attributes in face data.

Maintains high utility for identity, attractiveness, and smiling tasks.

Improves privacy and fairness metrics across benchmarks.

Abstract

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.