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On (The Lack Of) Location Privacy in Crowdsourcing Applications

Boukoros, Spyros and Humbert, Mathias and Katzenbeisser, Stefan and Troncoso, Carmela (2019):
On (The Lack Of) Location Privacy in Crowdsourcing Applications.
In: USENIX Security Symposium, Santa Clara, CA, USA, August 14–16, 2019, [Conference or Workshop Item]

Abstract

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. Furthermore, 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.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Boukoros, Spyros and Humbert, Mathias and Katzenbeisser, Stefan and Troncoso, Carmela
Title: On (The Lack Of) Location Privacy in Crowdsourcing Applications
Language: English
Abstract:

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. Furthermore, 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.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Security Engineering
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 2050 Privacy and Trust for Mobile Users
Profile Areas
Profile Areas > Cybersecurity (CYSEC)
Event Title: USENIX Security Symposium
Event Location: Santa Clara, CA, USA
Event Dates: August 14–16, 2019
Date Deposited: 07 May 2019 06:15
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