PAPER-HILT: Personalized and Adaptive Privacy-Aware Early-Exit for
  Reinforcement Learning in Human-in-the-Loop Systems
Mojtaba Taherisadr Salma Elmalaki
Reinforcement Learning (RL) has increasingly become a preferred method over
traditional rule-based systems in diverse human-in-the-loop (HITL) applications
due to its adaptability to the dynamic nature of human interactions. However,
integrating RL in such settings raises significant privacy concerns, as it
might inadvertently expose sensitive user information. Addressing this, our
paper focuses on developing PAPER-HILT, an innovative, adaptive RL strategy
through exploiting an early-exit approach designed explicitly for privacy
preservation in HITL environments. This approach dynamically adjusts the
tradeoff between privacy protection and system utility, tailoring its operation
to individual behavioral patterns and preferences. We mainly highlight the
challenge of dealing with the variable and evolving nature of human behavior,
which renders static privacy models ineffective. PAPER-HILT's effectiveness is
evaluated through its application in two distinct contexts: Smart Home
environments and Virtual Reality (VR) Smart Classrooms. The empirical results
demonstrate PAPER-HILT's capability to provide a personalized equilibrium
between user privacy and application utility, adapting effectively to
individual user needs and preferences. On average for both experiments, utility
(performance) drops by 24%, and privacy (state prediction) improves by 31%.