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Article

Inference attacks on location tracks

Author:

John KrummAuthors Info & Claims

PERVASIVE'07: Proceedings of the 5th international conference on Pervasive computing

Pages 127 - 143

Published: 13 May 2007 Publication History

Abstract

Although the privacy threats and countermeasures associated with location data are well known, there has not been a thorough experiment to assess the effectiveness of either. We examine location data gathered from volunteer subjects to quantify how well four different algorithms can identify the subjects' home locations and then their identities using a freely available, programmable Web search engine. Our procedure can identify at least a small fraction of the subjects and a larger fraction of their home addresses. We then apply three different obscuration countermeasures designed to foil the privacy attacks: spatial cloaking, noise, and rounding. We show how much obscuration is necessary to maintain the privacy of all the subjects.

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Cited By

Meteriz-Yildiran UYildiran NKim JMohaisen D(2024)Learning Location From Shared Elevation Profiles in Fitness Apps: A Privacy PerspectiveIEEE Transactions on Mobile Computing10.1109/TMC.2022.321814823:1(581-596)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMC.2022.3218148
Lu HZhao QChen YLiao XLin Z(2023)Detecting and Measuring Aggressive Location Harvesting in Mobile Apps via Data-flow Path EmbeddingProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35794477:1(1-27)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3579447
Rattay SCollins RSurana ALee YLiu YMauri AUrquhart LVines JWilson CPschetz LRozendaal MShklovski I(2023)Sensing Care Through Design: A Speculative Role-play Approach to "Living with" Sensor-supported Care NetworksProceedings of the 2023 ACM Designing Interactive Systems Conference10.1145/3563657.3596066(1660-1675)Online publication date: 10-Jul-2023
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Information & Contributors

Information

Published In

cover image Guide Proceedings

PERVASIVE'07: Proceedings of the 5th international conference on Pervasive computing

May 2007

369 pages

ISBN:9783540720362

Editors:
Anthony LaMarca
Intel Research Seattle, Seattle, WA
,
Marc Langheinrich
ETH Zurich, Institute for Pervasive Computing, Zurich, Switzerland
,
Khai N. Truong
University of Toronto, Department of Computer Science, Toronto, ON, Canada

Sponsors

SMART Technologies Inc.
FX Palo Alto Laboratory, Inc.: FX Palo Alto Laboratory, Inc.
Nokia
Google Inc.
Intel: Intel

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 13 May 2007

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Cited By

Meteriz-Yildiran UYildiran NKim JMohaisen D(2024)Learning Location From Shared Elevation Profiles in Fitness Apps: A Privacy PerspectiveIEEE Transactions on Mobile Computing10.1109/TMC.2022.321814823:1(581-596)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMC.2022.3218148
Lu HZhao QChen YLiao XLin Z(2023)Detecting and Measuring Aggressive Location Harvesting in Mobile Apps via Data-flow Path EmbeddingProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35794477:1(1-27)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3579447
Rattay SCollins RSurana ALee YLiu YMauri AUrquhart LVines JWilson CPschetz LRozendaal MShklovski I(2023)Sensing Care Through Design: A Speculative Role-play Approach to "Living with" Sensor-supported Care NetworksProceedings of the 2023 ACM Designing Interactive Systems Conference10.1145/3563657.3596066(1660-1675)Online publication date: 10-Jul-2023
https://dl.acm.org/doi/10.1145/3563657.3596066
Siddiqie SMondal AReddy P(2021)An Improved Dummy Generation Approach for Enhancing User Location PrivacyDatabase Systems for Advanced Applications10.1007/978-3-030-73200-4_33(487-495)Online publication date: 11-Apr-2021
https://dl.acm.org/doi/10.1007/978-3-030-73200-4_33
Boukoros SHumbert MKatzenbeisser STroncoso CHeninger NTraynor P(2019)On (the lack of) location privacy in crowdsourcing applicationsProceedings of the 28th USENIX Conference on Security Symposium10.5555/3361338.3361467(1859-1876)Online publication date: 14-Aug-2019
https://dl.acm.org/doi/10.5555/3361338.3361467
Stute MNarain SMariotto AHeinrich AKreitschmann DNoubir GHollick MHeninger NTraynor P(2019)A billion open interfaces for eve and malloryProceedings of the 28th USENIX Conference on Security Symposium10.5555/3361338.3361342(37-54)Online publication date: 14-Aug-2019
https://dl.acm.org/doi/10.5555/3361338.3361342
Luo YJia XDuan HWang CXu MFu S(2019)pRideProceedings of the International Symposium on Quality of Service10.1145/3326285.3329062(1-10)Online publication date: 24-Jun-2019
https://dl.acm.org/doi/10.1145/3326285.3329062
Zeni MZhang WBignotti EPasserini AGiunchiglia F(2019)Fixing Mislabeling by Human Annotators Leveraging Conflict Resolution and Prior KnowledgeProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33144193:1(1-23)Online publication date: 29-Mar-2019
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Meftah LRouvoy RChrisment I(2019)FOUGERE: User-Centric Location Privacy in Mobile Crowdsourcing AppsDistributed Applications and Interoperable Systems10.1007/978-3-030-22496-7_8(116-132)Online publication date: 17-Jun-2019
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(2018)Method of trajectory privacy protection based on restraining trajectory in LBSInternational Journal of Information and Communication Technology10.1504/IJICT.2018.09432113:3(329-339)Online publication date: 1-Jan-2018
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