Reinforcement learning (RL) presents numerous benefits compared to rule-based approaches in various applications. Privacy concerns have grown with the widespread use of RL trained with privacy-sensitive data in IoT devices, especially for human-in-the-loop systems. On the one hand, RL methods enhance the user experience by trying to adapt to the highly dynamic nature of humans. On the other hand, trained policies can leak the user's private information. Recent attention has been drawn to designing privacy-aware RL algorithms while maintaining an acceptable system utility. A central challenge in designing privacy-aware RL, especially for human-in-the-loop systems, is that humans have intrinsic variability and their preferences and behavior evolve. The effect of one privacy leak mitigation can be different for the same human or across different humans over time. Hence, we can not design one fixed model for privacy-aware RL that fits all. To that end, we propose adaPARL, an adaptive approach for privacy-aware RL, especially for human-in-the-loop IoT systems. adaPARL provides a personalized privacy-utility trade-off depending on human behavior and preference. We validate the proposed adaPARL on two IoT applications, namely (i) Human-in-the-Loop Smart Home and (ii) Human-in-the-Loop Virtual Reality (VR) Smart Classroom. Results obtained on these two applications validate the generality of adaPARL and its ability to provide a personalized privacy-utility trade-off. On average, for the first application, adaPARL improves the utility by $57\%$ over the baseline and by $43\%$ over randomization. adaPARL also reduces the privacy leak by $23\%$ on average. For the second application, adaPARL decreases the privacy leak to $44\%$ before the utility drops by $15\%$.

adaPARL是一种基于个性化隐私-效用权衡的自适应隐私感知强化学习方法，为物联网中的人机交互系统提供了一种用于应对隐私泄露的解决方案。在两种人机交互应用程序中，adaPARL提供了一种个性化的隐私-效用权衡，并证明了其具有很好的效果，使得实用性提高了57％，隐私泄露降低了23％。

adaPARL：面向序列决策人机交互系统的自适应隐私感知强化学习