Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

Abstract

With the maturity and development of 5G field, Mobile Edge CrowdSensing (MECS), as an intelligent data collection paradigm, provides a broad prospect for various applications in IoT. However, sensing users as data uploaders lack a balance between data benefits and privacy threats, leading to conservative data uploads and low revenue or excessive uploads and privacy breaches. To solve this problem, a Dynamic Privacy Measurement and Protection (DPMP) framework is proposed based on differential privacy and reinforcement learning. Firstly, a DPM model is designed to quantify the amount of data privacy, and a calculation method for personalized privacy threshold of different users is also designed. Furthermore, a Dynamic Private sensing data Selection (DPS) algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds. Finally, theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection, in particular, the proposed DPMP framework has 63% and 23% higher training efficiency and data benefits, respectively, compared to the Monte Carlo algorithm.