Secure Frameworks for User Motion Data in Virtual Reality

Abstract

Virtual Reality is an innovative technology transforming industries such as gaming, healthcare, and remote collaboration. The increasing deployment of these systems results in the continuous collection of telemetry data, including motion patterns, hand gestures, and spatial interactions. This data is valuable for enhancing user experiences and optimizing system performance. However, it also introduces significant privacy risks. Unlike traditional digital footprints, motion data captures fine-grained physical behaviors that can be linked to individual users, making anonymization ineffective in preventing re-identification.

This research introduces secure frameworks for user motion data in virtual reality, each proposed framework addressing privacy preservation from a different angle. The first framework uses synthetic data generation to replicate the statistical properties of real telemetry while significantly reducing user re-identification risk. The second framework applies encrypted analytics to enable computation without revealing raw input. Both frameworks are independently designed, implemented, and evaluated using data from a rhythm-based virtual-reality game that reflects natural user interaction.

Results from this research demonstrate that each approach preserves analytical utility while significantly reducing privacy risks. Together, these contributions establish a foundation for privacy-aligned telemetry analytics in immersive environments.