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

• URL: http://arxiv.org/abs/2207.07311v1
• Date: Fri, 15 Jul 2022 06:58:41 GMT
• Title: Towards Privacy-Preserving Person Re-identification via Person Identify Shift
• Authors: Shuguang Dou, Xinyang Jiang, Qingsong Zhao, Dongsheng Li, Cairong Zhao
• Abstract summary: Person re-identification (ReID) requires preserving the privacy of pedestrian images used by ReID methods. We propose a novel de-identification method designed explicitly for person ReID, named Person Identify Shift (PIS) PIS shifts each pedestrian image from the current identity to another with a new identity, resulting in images still preserving the relative identities.
• Score: 19.212691296927165
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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