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%.

翻译：在人本系统不断演进的背景下，由于人际互动的动态特性，个性化隐私解决方案正变得日益关键。传统的静态隐私模型往往难以满足用户多样且不断变化的隐私需求。本文提出PEaRL系统，该系统通过针对个体行为模式与偏好定制隐私策略来增强隐私保护能力。在引入强化学习（RL）以提升适应性的同时，PEaRL主要采用一种早期退出策略，动态平衡隐私保护与系统效用。该方法有效应对了人类行为多变性与演化性带来的挑战，而静态隐私模型难以妥善处理此类问题。我们在智能家居环境与虚拟现实（VR）智慧教室两种不同场景中对PEaRL进行评估。实证结果表明，PEaRL能够在用户隐私与应用效用之间实现个性化权衡，并有效适应个体用户偏好。在两种系统的平均测试中，PEaRL将隐私保护能力提升了31%，相应产生的效用损耗为24%。