Abstract

While spatial crowdsourcing has become a popular paradigm for spatio-temporal data collection, location privacy has raised increasing concerns among the participants of spatial crowdsourcing projects in recent years. The question of how to implement a spatial crowdsourcing project at minimal cost while preserving location privacy, is the major issue that most existing works have investigated. In this paper, we propose a novel privacy-preserving method for spatial crowdsourcing that combines location obfuscation and path optimization in order to provide enhanced privacy preservation at a minimal cost. We apply geo-indistinguishability and exponential mechanism to achieve an enhanced privacy guarantee. Moreover, because a higher privacy level consistently leads to extra distance cost, we therefore present a path optimization algorithm that reduces the total distance of a spatial crowdsourcing project. The experimental results demonstrate that the proposed method outperforms the traditional methods in terms of privacy level and performance costs.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.