Optimal de-anonymization in random graphs with community structure

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

Abstract

Anonymized social network graphs published for academic or advertisement purposes are subject to de-anonymization attacks by leveraging side information in the form of a second, public social network graph correlated with the anonymized graph. This is because the two are from the same underlying graph of true social relationships. In this paper, the maximum a posteriori (MAP) estimates of user identities for the anonymized graph are characterized and sufficient conditions for successful de-anonymization for underlying graphs with community structure are provided. The results generalize prior work that assumed underlying graphs of Erdíís-Renyi type, and prove the optimality of the attack strategy adopted in the literature.

Original languageEnglish (US)
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages709-713
Number of pages5
ISBN (Electronic)9781538639542
DOIs
StatePublished - Mar 1 2017
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: Nov 6 2016Nov 9 2016

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
CountryUnited States
CityPacific Grove
Period11/6/1611/9/16

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Cite this

Onaran, E., Garg, S., & Erkip, E. (2017). Optimal de-anonymization in random graphs with community structure. In M. B. Matthews (Ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 (pp. 709-713). [7869137] (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2016.7869137