Differentially Private Cross-camera Person Re-identification
Lucas Maris Yuki Matsuda Keiichi Yasumoto
Camera-based person re-identification is a heavily privacy-invading task by
design, benefiting from rich visual data to match together person
representations across different cameras. This high-dimensional data can then
easily be used for other, perhaps less desirable, applications. We here
investigate the possibility of protecting such image data against uses outside
of the intended re-identification task, and introduce a differential privacy
mechanism leveraging both pixelisation and colour quantisation for this
purpose. We show its ability to distort images in such a way that adverse task
performances are significantly reduced, while retaining high re-identification
performances.