On (The Lack Of) Location Privacy in Crowdsourcing Applications


Spyros Boukoros, TU-Darmstadt; Mathias Humbert, Swiss Data Science Center (ETH Zurich, EPFL); Stefan Katzenbeisser, TU-Darmstadt, University of Passau; Carmela Troncoso, EPFL


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 geo-located data though, 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.

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@inproceedings {236212,
author = {Spyros Boukoros and Mathias Humbert and Stefan Katzenbeisser and Carmela Troncoso},
title = {On (The Lack Of) Location Privacy in Crowdsourcing Applications},
booktitle = {28th USENIX Security Symposium (USENIX Security 19)},
year = {2019},
isbn = {978-1-939133-06-9},
address = {Santa Clara, CA},
pages = {1859--1876},
url = {https://www.usenix.org/conference/usenixsecurity19/presentation/boukoros},
publisher = {USENIX Association},
month = aug

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