ALPINE: A Lightweight and Adaptive Privacy-Decision Agent Framework for Dynamic Edge Crowdsensing

Mobile edge crowdsensing (MECS) systems continuously generate and transmit user data in dynamic, resource-constrained environments, exposing users to significant privacy threats. In practice, many privacy-preserving mechanisms build on differential privacy (DP). However, static DP mechanisms often fail to adapt to evolving risks, for example, shifts in adversarial capabilities, resource constraints and task requirements, resulting in either excessive noise or inadequate protection. To address this challenge, we propose ALPINE, a lightweight, adaptive framework that empowers terminal devices to autonomously adjust differential privacy levels in real time. ALPINE operates as a closed-loop control system consisting of four modules: dynamic risk perception, privacy decision via twin delayed deep deterministic policy gradient (TD3), local privacy execution and performance verification from edge nodes. Based on environmental risk assessments, we design a reward function that balances privacy gains, data utility and energy cost, guiding the TD3 agent to adaptively tune noise magnitude across diverse risk scenarios and achieve a dynamic equilibrium among privacy, utility and cost. Both the collaborative risk model and pretrained TD3-based agent are designed for low-overhead deployment. Extensive theoretical analysis and real-world simulations demonstrate that ALPINE effectively mitigates inference attacks while preserving utility and cost, making it practical for large-scale edge applications.