  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 $15\%$.
