Why Johnny Can't Develop Mobile Crowdsourcing Applications with Location Privacy
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Mobile crowdsourcing (MCS) relies on users' devices as sensors to perform geo-located data collection. Crowdsourcing enables application developers to benefit from large and diverse datasets at a low cost, making MCS extremely popular. The collection of geo-located data, however, raises serious privacy concerns for users. Yet, despite the large research body on location privacy-preserving mechanisms (LPPMs), MCS developers implement little to no protection for data collection or publication. In this paper we investigate the reason behind this gap between research and reality. We study the performance of existing LPPMs on publicly available data from two mobile crowdsourcing projects. Our results show that well-established defenses, designed with location-based services (LBSs) in mind, are either not applicable or only offer some protection in MCS. Furthermore, they have a strong impact on the utility of the applications. This is because these LPPMs are optimized for utility functions based on users' locations, while MCS utility functions depend on the values (e.g., measurements) associated with those locations. We hope that this work raises awareness in the community about an overlooked problem such that soon we have new LPPMs that can help MCS developers to protect the privacy of their users.
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