How to balance VR streaming privacy and quality?
Optimizing QoE-Privacy Tradeoff for Proactive VR Streaming
This paper addresses the privacy-QoE tradeoff in proactive VR streaming, where uploading user viewpoint data for predictive streaming exposes sensitive information. Existing privacy-preserving methods, which add noise to viewpoints, compromise QoE and don't fully eliminate leakage. This research proposes a novel approach (B-PEA) that adds noise to the prediction error instead of the viewpoint itself, thereby breaking the link between uploaded data and actual viewpoint. This allows for achieving zero viewpoint leakage with minimal QoE impact.
While not explicitly about multi-agent systems, this research relates to LLM-based multi-agent systems in that it tackles a core challenge of distributed systems: balancing data utility (here, QoE) with privacy. The B-PEA method could inspire similar strategies in multi-agent LLM applications where agents need to share information (analogous to viewpoint data) for collaborative tasks without revealing sensitive underlying details. The concept of perturbing derived information rather than raw data could translate to protecting sensitive inferences derived from LLM outputs in multi-agent communication.