  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.
