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Differential Privacy and k-Anonymity for Pedestrian Image Data: Impact on Cross-Camera Person Re-Identification and Demographic Predictions

Lucas Maris,Yuki Matsuda,K. Yasumoto

Published 2025 in ACM Trans. Cyber Phys. Syst.

ABSTRACT

Video cameras are prevalent in large cities but their use outside of public safety remains limited due to legitimate privacy concerns. Nevertheless, the rich information they can capture appears incredibly promising for large-scale smart city applications, as they can function as very powerful and versatile sensors. This ambivalence raises the question of whether such image data can be used in a privacy-responsible manner. Encryption-based solutions assume the end server can be trusted with keeping data safe; data leaks show us this assumption does not necessarily hold true. Traditional image obfuscation methods such as pixelization or blurring on the other hand fail to offer both sufficient privacy and utility. As such, privacy approaches that can provide privacy protection directly on the data itself while retaining practical utility are required. We here extend two such notions, differential privacy and \( k \) -anonymity, to image data, and extensively evaluate the resulting privacy-utility tradeoff on cross-camera person re-identification and attribute recognition data. Our results show that our proposed approaches can significantly reduce the privacy-sensitivity of image data at source while retaining decent utility for vision-based smart city applications.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    ACM Trans. Cyber Phys. Syst.

  • Publication date

    2025-06-10

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.1145/3743680
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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