Optimal Active social Network De-anonymization Using Information Thresholds

Farhad Shirani, Siddharth Garg, Elza Erkip

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

Abstract

In this paper, de-anonymizing internet users by actively querying their group memberships in social networks is considered. An anonymous victim visits the attacker's website, and the attacker uses the victim's browser history to query her social media activity for the purpose of de-anonymization using the minimum number of queries. A stochastic model of the problem is considered where the attacker has partial prior knowledge of the group membership graph and receives noisy responses to its real-time queries. The victim's identity is assumed to be chosen randomly based on a given distribution which models the users' risk of visiting the malicious website. A de-anonymization algorithm is proposed which operates based on information thresholds and its performance both in the finite and asymptotically large social network regimes is analyzed. Furthermore, a converse result is provided which proves the optimality of the proposed attack strategy.

Original languageEnglish (US)
Title of host publication2018 IEEE International Symposium on Information Theory, ISIT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1445-1449
Number of pages5
Volume2018-June
ISBN (Print)9781538647806
DOIs
StatePublished - Aug 15 2018
Event2018 IEEE International Symposium on Information Theory, ISIT 2018 - Vail, United States
Duration: Jun 17 2018Jun 22 2018

Other

Other2018 IEEE International Symposium on Information Theory, ISIT 2018
CountryUnited States
CityVail
Period6/17/186/22/18

Fingerprint

Social Networks
Websites
Query
Stochastic models
Social Media
Internet
Prior Knowledge
Converse
Stochastic Model
Optimality
Attack
Real-time
Partial
Graph in graph theory
Model
Strategy
History

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

Cite this

Shirani, F., Garg, S., & Erkip, E. (2018). Optimal Active social Network De-anonymization Using Information Thresholds. In 2018 IEEE International Symposium on Information Theory, ISIT 2018 (Vol. 2018-June, pp. 1445-1449). [8437739] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2018.8437739

Optimal Active social Network De-anonymization Using Information Thresholds. / Shirani, Farhad; Garg, Siddharth; Erkip, Elza.

2018 IEEE International Symposium on Information Theory, ISIT 2018. Vol. 2018-June Institute of Electrical and Electronics Engineers Inc., 2018. p. 1445-1449 8437739.

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

Shirani, F, Garg, S & Erkip, E 2018, Optimal Active social Network De-anonymization Using Information Thresholds. in 2018 IEEE International Symposium on Information Theory, ISIT 2018. vol. 2018-June, 8437739, Institute of Electrical and Electronics Engineers Inc., pp. 1445-1449, 2018 IEEE International Symposium on Information Theory, ISIT 2018, Vail, United States, 6/17/18. https://doi.org/10.1109/ISIT.2018.8437739
Shirani F, Garg S, Erkip E. Optimal Active social Network De-anonymization Using Information Thresholds. In 2018 IEEE International Symposium on Information Theory, ISIT 2018. Vol. 2018-June. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1445-1449. 8437739 https://doi.org/10.1109/ISIT.2018.8437739
Shirani, Farhad ; Garg, Siddharth ; Erkip, Elza. / Optimal Active social Network De-anonymization Using Information Thresholds. 2018 IEEE International Symposium on Information Theory, ISIT 2018. Vol. 2018-June Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1445-1449
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