SecureReID: Privacy-Preserving Anonymization for Person Re-Identification

Anonymization methods have gained widespread use in safeguarding privacy. However, conventional anonymization solutions inevitably lead to the loss of semantic information, resulting in limited data utility. Besides, existing deep learning-based anonymization strategies inadvertently alter the identities of pedestrians, rendering them unsuitable for re-identification (Re-ID) tasks. Beyond these limitations, we propose a joint learning reversible anonymization framework that can reversibly generate full-body anonymized images with little performance drop on Re-ID tasks. Despite these advancements, we reveal that the anonymization methods are vulnerable to model attacks, where attackers can utilize the anonymization model and public data to perform recovery and Re-ID tasks on anonymized images. To defend against the potential attack, we introduce the identity-specific encrypt-decrypt (ISED) architecture for enhanced security, where the anonymized images are encrypted using the specific key for each identity. It renders the images computationally inaccessible to attackers while allowing for seamless reversal without loss using the corresponding keys. Extensive experiments demonstrate that the anonymization framework can guarantee Re-ID performance while protecting pedestrian privacy. In addition, we provide both empirical and theoretical evidence to demonstrate the feasibility of model attacks and the effectiveness of our ISED strategy. Code is available at https://github.com/shentt67/SecureReID.