Privacy-Preserving Task Allocation for Edge Computing Enhanced Mobile Crowdsensing

In traditional mobile crowdsensing (MCS) applications, the crowdsensing server (CS-server) need mobile users' precise locations for optimal task allocation, which raises privacy concerns. This work proposes a framework P2TA to optimize task acceptance rate while protecting users' privacy. Specifically, edge nodes are introduced as an anonymous server and a task allocation agent to prevent CS-server from directly obtaining user data and dispersing privacy risks. On this basis, a genetic algorithm that performed on edge nodes is designed to choose an initial obfuscation strategy. Furthermore, a privacy game model is used to optimize user/adversary objectives against each other to obtain a final obfuscation strategy which can be immune to posterior inference. Finally, edge nodes take user acceptance rate and task allocation rate into account comprehensively, focusing on maximizing the expected accepted task number under the constraint of differential privacy and distortion privacy. The effectiveness and superiority of P2TA to the exiting MCS task allocation schemes are validated via extensive simulations on the synthetic data, as well as the measured data collected by ourselves.