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Sensors 2015, 15(7), 15285-15310; doi:10.3390/s150715285

Location Privacy for Mobile Crowd Sensing through Population Mapping

1
Myongji University, Myongjiro 116, Yongin 449-728, Korea
2
Intel Labs, Hillsboro, OR 97124, USA
3
Indiana University, Bloomington, IN 47408, USA
4
Boston University, 111 Cummington Mall, Boston, MA 02215, USA
5
Dartmouth College, Hanover, NH 03755, USA
This article is an expanded version of an earlier conference paper: Kapadia, A.; Triandopoulos, N.; Cornelius, C.; Peebles, D.; Kotz, D. AnonySense: Opportunistic and Privacy-Preserving Context Collection. In Proceedings of the International Conference on Pervasive Computing (Pervasive), Sydney, Australia, 19–22 May 2008, pp. 280–297. While the core idea of using tiles remains the same, this article represents a complete redesign of the algorithms and provides a thorough (re)evaluation of the same.
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio Puliafito
Received: 30 April 2015 / Revised: 18 June 2015 / Accepted: 19 June 2015 / Published: 29 June 2015
(This article belongs to the Special Issue Sensors and Smart Cities)
View Full-Text   |   Download PDF [607 KB, uploaded 29 June 2015]   |  

Abstract

Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users’ mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users’ privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces. View Full-Text
Keywords: location privacy; k-anonymity; mobility traces location privacy; k-anonymity; mobility traces
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Shin, M.; Cornelius, C.; Kapadia, A.; Triandopoulos, N.; Kotz, D. Location Privacy for Mobile Crowd Sensing through Population Mapping. Sensors 2015, 15, 15285-15310.

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