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<p>Recently, privacy concerns of person re-identification (ReID) have raised more and more attention, and protecting personal information in the privacy-sensitive images used by ReID methods has become essential. In order to utilize data from video surveillance without leaking pedestrians’ private information, person de-identification (DeID) is a simple and effective method of alleviating privacy issues by removing identity-related information from the data. Most of the existing DeID methods focus on identity-irrelevant tasks such as pose and action recognition and tend to remove all identity-related information. However, this compromises the usability of de-identified data in the ReID task. In this paper, we aim to develop a technique to 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 identity 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 the variational auto-encoder. Experimental results show that our method has a better trade-off between privacy-preserving and model performance compared to existing de-identification methods and can defend against human and model attacks for data privacy. The codebase of PIS is available at <a href="https://github.com/Vill-Lab/2025-SCIS-PIS">https://github.com/Vill-Lab/2025-SCIS-PIS</a>.</p>

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Person identity shift for privacy-preserving person re-identification

  • 期刊论文

  • 发表日期：

    2026年1月4日

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  • Shuguang Dou,
  • Xinyang Jiang,
  • Qingsong Zhao,
  • Yansen Wang,
  • Dongsheng Li,
  • Cairong Zhao

摘要

Recently, privacy concerns of person re-identification (ReID) have raised more and more attention, and protecting personal information in the privacy-sensitive images used by ReID methods has become essential. In order to utilize data from video surveillance without leaking pedestrians’ private information, person de-identification (DeID) is a simple and effective method of alleviating privacy issues by removing identity-related information from the data. Most of the existing DeID methods focus on identity-irrelevant tasks such as pose and action recognition and tend to remove all identity-related information. However, this compromises the usability of de-identified data in the ReID task. In this paper, we aim to develop a technique to 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 identity 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 the variational auto-encoder. Experimental results show that our method has a better trade-off between privacy-preserving and model performance compared to existing de-identification methods and can defend against human and model attacks for data privacy. The codebase of PIS is available at https://github.com/Vill-Lab/2025-SCIS-PIS.