research-article

Identification via location-profiling in GSM networks

Authors:

Yoni De Mulder,

George Danezis,

Lejla Batina,

Bart PreneelAuthors Info & Claims

WPES '08: Proceedings of the 7th ACM workshop on Privacy in the electronic society

Pages 23 - 32

https://doi.org/10.1145/1456403.1456409

Published: 27 October 2008 Publication History

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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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Gadotti ARocher LHoussiau FCreţu Ade Montjoye Y(2024)Anonymization: The imperfect science of using data while preserving privacyScience Advances10.1126/sciadv.adn705310:29Online publication date: 19-Jul-2024
https://doi.org/10.1126/sciadv.adn7053
Eshun SPalmieri P(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
https://doi.org/10.1080/17489725.2024.2385312
Wiedemann NMartin HSuel EHong YXin Y(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
https://doi.org/10.1080/17489725.2023.2239190
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Index Terms

Identification via location-profiling in GSM networks
1. Security and privacy
  1. Human and societal aspects of security and privacy
2. Social and professional topics
  1. Computing / technology policy
    1. Computer crime
    2. Privacy policies

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Published In

WPES '08: Proceedings of the 7th ACM workshop on Privacy in the electronic society

October 2008

128 pages

ISBN:9781605582894

DOI:10.1145/1456403

General Chair:
Vijay Atluri
Rutgers University, USA
,
Program Chair:
Marianne Winslett
University of Illinois at Urbana-Champaign, USA

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

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Published: 27 October 2008

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CCS08: 15th ACM Conference on Computer and Communications Security 2008

October 27, 2008

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Overall Acceptance Rate 106 of 355 submissions, 30%

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Gadotti ARocher LHoussiau FCreţu Ade Montjoye Y(2024)Anonymization: The imperfect science of using data while preserving privacyScience Advances10.1126/sciadv.adn705310:29Online publication date: 19-Jul-2024
https://doi.org/10.1126/sciadv.adn7053
Eshun SPalmieri P(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
https://doi.org/10.1080/17489725.2024.2385312
Wiedemann NMartin HSuel EHong YXin Y(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
https://doi.org/10.1080/17489725.2023.2239190
Tournier Ade Montjoye Y(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
https://doi.org/10.1126/sciadv.abl6464
Eshun SPalmieri P(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
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Wang HLi YWang GJin D(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
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Wang HLi YGao CWang GTao XJin D(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
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Cao YXiao YXiong LBai LYoshikawa M(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
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Sung KLevine BZheleva MMayrhofer RRoland M(2020)Protecting location privacy from untrusted wireless service providersProceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks10.1145/3395351.3399369(266-277)Online publication date: 8-Jul-2020
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Protecting location privacy using location semantics

Realtime location privacy via mobility prediction: creating confusion at crossroads

A privacy-aware location cloaking technique reducing bandwidth consumption in location-based services

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