  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.
