Area coverage-based worker recruitment under geo-indistinguishability

Pengfei Zhang, Xiang Cheng, Sen Su, Ning Wang

Abstract: Location information is usually required for area coverage-based worker recruitment in mobile crowdsensing, which may pose considerable threats to individual privacy without proper privacy protection. In this paper, we investigate the problem of area coverage-based worker recruitment under geo-indistinguishability while considering each participant’s sensing radius, which aims to select a suitable set of participants under a worker number constraint to achieve the maximum coverage ratio for a target region. To this end, we present a geo-indiStinguishable arEa Coverage-based workeR rEcruitmenT approach, referred to as SECRET. In SECRET, to protect each participant’s location, we develop an optimized geographical exponential mechanism OptGEM with solid privacy and utility guarantees. To select the recruited workers based on the obfuscated locations while ensuring large coverage for the target region, we design a coverage-aware worker selection method CWS. We show SECRET satisfies a discrete version of ɛ<math><mi is="true">ɛ</mi></math>-geo-indistinguishability. Extensive experiments on two real-world datasets and a synthetic dataset confirm the effectiveness of SECRET.