User Privacy in Skeleton-based Motion Data

Thomas Carr, Depeng Xu

Abstract: Capturing skeleton data is an important area of computer vision, especially for use in a virtual reality (VR) setting. As new techniques to extract skeletons come out, the popularity of skeleton-based motion data has increased. While the skeleton data appears to be anonymous, it can be exploited to discover personally identifiable information (PII). This poses a risk of unintentional privacy leakages when skeletons are publicly displayed, like in a VR environment. We explore the privacy implications posed by the skeleton data, focusing on the privacy and utility trade-off and current privacy-preserving techniques. In this paper, we propose a new baseline attack model Linkage Attack Neural Network (LAN) that acts as a matching classifier to distinguish whether two skeleton-sequences are from the same actor. Then we propose a new defense model, the Privacy-centric Deep Motion Retargeting (PMR) model that is an adversarial/cooperatively trained motion retargeting model. Additionally, we incorporate explanation techniques to identify and mask the most privacy-sensitive joints, either by zeroing them out or adding controlled noise. Finally, we propose a transformer-based motion retargeting model designed for real-time applications, leveraging autoregressive decoding for continuous, frame-by-frame skeleton generation.