Privacy-Enhancing Person Re-Identification Framework - A Dual-Stage Approach

Abstract

In this work, we show that deep learning-based re-identification (Re-ID) models, albeit trained only with a Re-ID objective (i.e. if two samples belong to the same identity), encode personally identifiable information (PII) in the learned features that may lead to serious privacy concerns. In cognizance of the modern privacy regulations on protecting PII, we propose a novel dual-stage person Re-ID framework that (1) suppresses the PII from the discriminative features, and (2) introduces a controllable privacy mechanism through differential privacy. The former is achieved with a self-supervised de-identification (De-ID) decoder and an adversarial-identity (Adv-ID) module, whereas the latter mechanism leverages a controllable privacy budget to generate a privacy-protected gallery with a Gaussian noise generator. Furthermore, we introduce the notion of a privacy metric to quantify the privacy leakage in Re-ID features which is not explicitly examined in prior work. We demonstrate the feasibility of our approach in achieving a better trade-off between utility and privacy through rigorous experiments on person Re-ID benchmarks.