PEaRL: Personalized Privacy of Human-Centric Systems using Early-Exit Reinforcement Learning
This addresses personalized privacy for users in human-centric systems like smart homes and VR classrooms, but it is incremental as it builds on reinforcement learning with an early-exit strategy.
The paper tackled the problem of static privacy models failing to meet dynamic user needs in human-centric systems by introducing PEaRL, which uses an early-exit reinforcement learning strategy to personalize privacy, resulting in an average 31% enhancement in privacy protection with a 24% utility reduction across smart home and VR classroom contexts.
In the evolving landscape of human-centric systems, personalized privacy solutions are becoming increasingly crucial due to the dynamic nature of human interactions. Traditional static privacy models often fail to meet the diverse and changing privacy needs of users. This paper introduces PEaRL, a system designed to enhance privacy preservation by tailoring its approach to individual behavioral patterns and preferences. While incorporating reinforcement learning (RL) for its adaptability, PEaRL primarily focuses on employing an early-exit strategy that dynamically balances privacy protection and system utility. This approach addresses the challenges posed by the variability and evolution of human behavior, which static privacy models struggle to handle effectively. We evaluate PEaRL in two distinct contexts: Smart Home environments and Virtual Reality (VR) Smart Classrooms. The empirical results demonstrate PEaRL's capability to provide a personalized tradeoff between user privacy and application utility, adapting effectively to individual user preferences. On average, across both systems, PEaRL enhances privacy protection by 31%, with a corresponding utility reduction of 24%.