Towards Privacy-Preserving Person Re-identification via Person Identify Shift

TL;DR

This paper introduces Person Identify Shift (PIS), a novel de-identification technique for person re-identification that balances privacy protection with data usability by shifting identities while preserving relative identity relationships.

Contribution

The paper proposes PIS, a new de-identification method that maintains relative identities in pedestrian images, improving privacy protection without sacrificing ReID performance.

Findings

PIS outperforms existing de-identification methods in privacy-utility trade-off.

PIS effectively defends against human and model attacks.

Experimental results demonstrate improved balance between privacy and model accuracy.

Abstract

Recently privacy concerns of person re-identification (ReID) raise more and more attention and preserving the privacy of the pedestrian images used by ReID methods become essential. De-identification (DeID) methods alleviate privacy issues by removing the identity-related of the ReID data. However, most of the existing DeID methods tend to remove all personal identity-related information and compromise the usability of de-identified data on the ReID task. In this paper, we aim to develop a technique that can achieve a good trade-off between privacy protection and data usability for person ReID. To achieve this, we propose a novel de-identification method designed explicitly for person ReID, named Person Identify Shift (PIS). PIS removes the absolute identity in a pedestrian image while preserving the identity relationship between image pairs. By exploiting the interpolation property of variational auto-encoder, PIS shifts each pedestrian image from the current identity to another with a new identity, resulting in images still preserving the relative identities. Experimental results show that our method has a better trade-off between privacy-preserving and model performance than existing de-identification methods and can defend against human and model attacks for data privacy.