Always Protect You: Privacy-Preserving Sparse Mobile Crowdsensing with Location and Data

Sparse Mobile Crowdsensing (MCS) offers a promising paradigm for data collection by leveraging mobile devices to collect data from strategically selected subareas.While this approach reduces the overall data collection burden, challenges arise in effectively selecting these target subareas.Existing sparse MCS techniques often necessitate participants to disclose their location and collected data, raising significant privacy concerns.To this end, we propose a novel privacy-preserving sparse MCS framework that safeguards both location and data privacy, named PEAR.PEAR employs a seamless integration of additive secret sharing and function secret sharing to ensure protection of location and data privacy.Inspired by the performance of reinforcement learning (RL) in subarea selection, we design a privacy-preserving subarea selection algorithm based on RL, which can select a target subarea while ensuring the location privacy of the subarea.Experimental evaluations over a real dataset show the effectiveness and efficiency of PEAR. CCS Concepts Security and privacy Domain-specific security and