DCentroid: Location Privacy-Preserving Scheme in Spatial Crowdsourcing

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Abstract
Spatial Crowdsourcing (SC) nowadays has become a valuable paradigm, relies on crowd workers to perform a set of spatial-temporal tasks at specific locations. However, existing solutions require the crowd workers to disclose their precise locations to untrustworthy service providers, which leads to location privacy concerns. This paper, introduces (DCentroid), a novel framework that aims to preserve location privacy for the crowd workers in SC. The proposed framework adapts an anonymous communication technique using dummy based technique to generate dummy locations that send to the service providers. This paper theoretically analyzes DCentroid framework and guarantee the crowd workers privacy while preserving the functionality of SC such as task assignment rate, worker travel distance and system overhead.

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