Mitigating Privacy Threats Without Degrading Visual Quality of VR Applications: Using Re-Identification Attack as a Case Study

Virtual Reality (VR) applications take users' Head-Mounted Display (HMD) and controller trajectories as inputs for an immersive experience. Leakage of these trajectories threatens user privacy in several aspects, including but not limited to their identities. Existing privacy-preserving approaches, however, overlook the temporal correlation of VR user trajectories, which could be leveraged by attackers. In this paper, we develop a disturber to perturb VR user trajectories in both temporal and spatial domains on the fly. Such trajectory perturbations could, unfortunately, lead to distorted rendered VR viewports. Thus, we develop a compensator to recover from such distortion using efficient image-warping algorithms. Our evaluation results show the merits of our proposed solution: (i) our disturber alone reduces at most 0.42 re-identification rate of VR users compared to the state-of-the-art approach, (ii) our disturber alone outperforms the state-of-the-art approach by 2.43 dB in PSNR, 0.13 in SSIM, and 8.15 in VMAF under the same privacy-preserving settings, and (iii) our compensator further boosts the visual quality of a VR application by at most 6.83 dB in PSNR, 0.45 in SSIM, and 34.57 in VMAF, compared to disturber-only solution.