On (The Lack Of) Location Privacy in Crowdsourcing Applications
This work addresses privacy risks for users in mobile crowdsourcing applications, highlighting a critical gap in current protections and calling for new solutions to prevent widespread privacy breaches.
The study investigated the mismatch between existing location privacy-preserving mechanisms (LPPMs) and mobile crowdsourcing (MCS) applications, finding that these defenses are often inapplicable or ineffective in MCS settings and significantly reduce utility more than expected, as they are optimized for location-based services rather than MCS-specific needs.
Crowdsourcing enables application developers to benefit from large and diverse datasets at a low cost. Specifically, mobile crowdsourcing (MCS) leverages users' devices as sensors to perform geo-located data collection. The collection of geolocated data 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. To understand this mismatch, we study the performance of existing LPPMs on publicly available data from two mobile crowdsourcing projects. Our results show that well-established defenses are either not applicable or offer little protection in the MCS setting. Additionally, they have a much stronger impact on applications' utility than foreseen in the literature. This is because existing LPPMs, designed with location-based services (LBSs) in mind, 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 finally outline possible research avenues to facilitate the development of new location privacy solutions that fit the needs of MCS so that the increasing number of such applications do not jeopardize their users' privacy.