Privacy concerns from variances in spatial navigability in VR

TL;DR

This paper explores privacy issues in VR caused by user movement data and proposes machine learning algorithms to address these concerns, with potential applications in AR.

Contribution

It introduces machine learning methods designed to protect user privacy in VR and AR environments by learning cooperatively with users.

Findings

Machine learning algorithms can help mitigate privacy risks in VR.

The approach can be extended to AR platforms.

Potential for improved privacy-preserving navigation systems.

Abstract

Current Virtual Reality (VR) input devices make it possible to navigate a virtual environment and record immersive, personalized data regarding the user's movement and specific behavioral habits, which brings the question of the user's privacy concern to the forefront. In this article, the authors propose to investigate Machine Learning driven learning algorithms that try to learn with human users co-operatively and can be used to countermand existing privacy concerns in VR but could also be extended to Augmented Reality (AR) platforms.