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Identification via location-profiling in GSM networks

Published: 27 October 2008 Publication History

Abstract

As devices move within a cellular network, they register their new location with cell base stations to allow for the correct forwarding of data. We show it is possible to identify a mobile user from these records and a pre-existing location profile, based on previous movement. Two different identification processes are studied, and their performances are evaluated on real cell location traces. The best of those allows for the identification of around 80% of users. We also study the misidentified users and characterise them using hierarchical clustering techniques. Our findings highlight the difficulty of anonymizing location data, and firmly establish they are personally identifiable.

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Cited By

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  • (2024)Anonymization: The imperfect science of using data while preserving privacyScience Advances10.1126/sciadv.adn705310:29Online publication date: 19-Jul-2024
  • (2024)De-anonymisation of real-world location traces: two attacks based on the hidden Markov modelJournal of Location Based Services10.1080/17489725.2024.2385312(1-30)Online publication date: 11-Aug-2024
  • (2024)An overview of proposals towards the privacy-preserving publication of trajectory dataInternational Journal of Information Security10.1007/s10207-024-00894-023:6(3711-3747)Online publication date: 4-Sep-2024
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Published In

cover image ACM Conferences
WPES '08: Proceedings of the 7th ACM workshop on Privacy in the electronic society
October 2008
128 pages
ISBN:9781605582894
DOI:10.1145/1456403
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2008

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Author Tags

  1. cellular network
  2. identification
  3. location privacy
  4. location profile

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Overall Acceptance Rate 106 of 355 submissions, 30%

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Cited By

View all
  • (2024)Anonymization: The imperfect science of using data while preserving privacyScience Advances10.1126/sciadv.adn705310:29Online publication date: 19-Jul-2024
  • (2024)De-anonymisation of real-world location traces: two attacks based on the hidden Markov modelJournal of Location Based Services10.1080/17489725.2024.2385312(1-30)Online publication date: 11-Aug-2024
  • (2024)An overview of proposals towards the privacy-preserving publication of trajectory dataInternational Journal of Information Security10.1007/s10207-024-00894-023:6(3711-3747)Online publication date: 4-Sep-2024
  • (2023)Influence of tracking duration on the privacy of individual mobility graphsJournal of Location Based Services10.1080/17489725.2023.223919017:4(370-388)Online publication date: 27-Jul-2023
  • (2022)Expanding the attack surface: Robust profiling attacks threaten the privacy of sparse behavioral dataScience Advances10.1126/sciadv.abl64648:33Online publication date: 19-Aug-2022
  • (2022)Two de-anonymization attacks on real-world location data based on a hidden Markov model2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW55150.2022.00062(01-09)Online publication date: Jun-2022
  • (2021)Linking Multiple User Identities of Multiple Services from Massive Mobility TracesACM Transactions on Intelligent Systems and Technology10.1145/343981712:4(1-28)Online publication date: 12-Aug-2021
  • (2021)Anonymization and De-Anonymization of Mobility Trajectories: Dissecting the Gaps Between Theory and PracticeIEEE Transactions on Mobile Computing10.1109/TMC.2019.295277420:3(796-815)Online publication date: 1-Mar-2021
  • (2021)Protecting Spatiotemporal Event Privacy in Continuous Location-Based ServicesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.296331233:8(3141-3154)Online publication date: 1-Aug-2021
  • (2021)De-anonymization Attack Method of Mobility Trajectory Data Based on Semantic Trajectory PatternMobile Multimedia Communications10.1007/978-3-030-89814-4_26(354-366)Online publication date: 2-Nov-2021
  • Show More Cited By

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