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.

翻译：基于摄像头的人体重识别是一项本质上高度侵犯隐私的任务，它依赖丰富的视觉数据在不同摄像头之间匹配行人表征。这种高维数据很容易被用于其他可能不太合适的应用场景。本文研究了保护此类图像数据免受预期重识别任务之外滥用的可能性，并引入了一种结合像素化与色彩量化的差分隐私机制。我们证明，该方法能够在显著降低恶意任务性能的同时，保持较高的重识别性能。