Deanonymizing mobility traces with co-location information

Youssef Khazbak, Guohong Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Mobility traces have been widely used in the design and evaluation of mobile networks. To mitigate the privacy threat of publishing mobility traces, the traces are often anonymized and obfuscated. However, even with anonymization and obfuscation techniques, traces can still be deanonymized by exploiting some side information such as users' co-location. With online social networks, mobile users increasingly report their co-locations with other users. For example, a user may report being with friends at a restaurant for lunch or dinner, and hence his friends' location information can be inferred. To find out whether co-location information can be exploited to identify a user and reveal his behavior from a set of mobility traces, we use a dataset from Twitter and Swarm to illustrate how an adversary can gather side information consisting of users' location and co-location. Based on the collected information, the adversary can run a simple yet effective location inference attack. We generalize this attack, formulate the identity inference problem, and develop inference attacks, under different observed side information, that deem effective in identifying the users. We perform comprehensive experimental analysis based on real datasets for taxi cabs and buses. The evaluation results show that co-location information can be used to significantly improve the accuracy of the identity inference attack.

Original languageEnglish (US)
Title of host publication2017 IEEE Conference on Communications and Network Security, CNS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-9
Number of pages9
ISBN (Electronic)9781538606834
DOIs
StatePublished - Dec 19 2017
Event2017 IEEE Conference on Communications and Network Security, CNS 2017 - Las Vegas, United States
Duration: Oct 9 2017Oct 11 2017

Publication series

Name2017 IEEE Conference on Communications and Network Security, CNS 2017
Volume2017-January

Other

Other2017 IEEE Conference on Communications and Network Security, CNS 2017
CountryUnited States
CityLas Vegas
Period10/9/1710/11/17

Fingerprint

Wireless networks

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

Cite this

Khazbak, Y., & Cao, G. (2017). Deanonymizing mobility traces with co-location information. In 2017 IEEE Conference on Communications and Network Security, CNS 2017 (pp. 1-9). (2017 IEEE Conference on Communications and Network Security, CNS 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CNS.2017.8228621
Khazbak, Youssef ; Cao, Guohong. / Deanonymizing mobility traces with co-location information. 2017 IEEE Conference on Communications and Network Security, CNS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-9 (2017 IEEE Conference on Communications and Network Security, CNS 2017).
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Khazbak, Y & Cao, G 2017, Deanonymizing mobility traces with co-location information. in 2017 IEEE Conference on Communications and Network Security, CNS 2017. 2017 IEEE Conference on Communications and Network Security, CNS 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-9, 2017 IEEE Conference on Communications and Network Security, CNS 2017, Las Vegas, United States, 10/9/17. https://doi.org/10.1109/CNS.2017.8228621

Deanonymizing mobility traces with co-location information. / Khazbak, Youssef; Cao, Guohong.

2017 IEEE Conference on Communications and Network Security, CNS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-9 (2017 IEEE Conference on Communications and Network Security, CNS 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Mobility traces have been widely used in the design and evaluation of mobile networks. To mitigate the privacy threat of publishing mobility traces, the traces are often anonymized and obfuscated. However, even with anonymization and obfuscation techniques, traces can still be deanonymized by exploiting some side information such as users' co-location. With online social networks, mobile users increasingly report their co-locations with other users. For example, a user may report being with friends at a restaurant for lunch or dinner, and hence his friends' location information can be inferred. To find out whether co-location information can be exploited to identify a user and reveal his behavior from a set of mobility traces, we use a dataset from Twitter and Swarm to illustrate how an adversary can gather side information consisting of users' location and co-location. Based on the collected information, the adversary can run a simple yet effective location inference attack. We generalize this attack, formulate the identity inference problem, and develop inference attacks, under different observed side information, that deem effective in identifying the users. We perform comprehensive experimental analysis based on real datasets for taxi cabs and buses. The evaluation results show that co-location information can be used to significantly improve the accuracy of the identity inference attack.

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Khazbak Y, Cao G. Deanonymizing mobility traces with co-location information. In 2017 IEEE Conference on Communications and Network Security, CNS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-9. (2017 IEEE Conference on Communications and Network Security, CNS 2017). https://doi.org/10.1109/CNS.2017.8228621