Blind De-anonymization Attacks using Social Networks

Wei Han Lee, Shouling Ji, Changchang Liu, Prateek Mittal, Ruby B. Lee

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

7 Scopus citations

Abstract

It is important to study the risks of publishing privacy-sensitive data. Even if sensitive identities (e.g., name, social security number) were removed and advanced data perturbation techniques were applied, several de-anonymization attacks have been proposed to re-identify individuals. However, existing attacks have some limitations: 1) they are limited in de-anonymization accuracy; 2) they require prior seed knowledge and suffer from the imprecision of such seed information. We propose a novel structure-based de-anonymization attack, which does not require the attacker to have prior information (e.g., seeds). Our attack is based on two key insights: using multihop neighborhood information, and optimizing the process of deanonymization by exploiting enhanced machine learning techniques. The experimental results demonstrate that our method is robust to data perturbations and significantly outperforms the stateof- the-art de-anonymization techniques by up to 10× improvement.

Original languageEnglish (US)
Title of host publicationWPES 2017 - Proceedings of the 2017 Workshop on Privacy in the Electronic Society, co-located with CCS 2017
PublisherAssociation for Computing Machinery, Inc
Pages1-4
Number of pages4
ISBN (Electronic)9781450351751
DOIs
StatePublished - Oct 30 2017
Event16th ACM Workshop on Privacy in the Electronic Society, WPES 2017 - Dallas, United States
Duration: Oct 30 2017 → …

Publication series

NameWPES 2017 - Proceedings of the 2017 Workshop on Privacy in the Electronic Society, co-located with CCS 2017
Volume2017-January

Other

Other16th ACM Workshop on Privacy in the Electronic Society, WPES 2017
CountryUnited States
CityDallas
Period10/30/17 → …

All Science Journal Classification (ASJC) codes

  • Social Sciences (miscellaneous)
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

Keywords

  • De-anonymization
  • Graph anonymity
  • Machine learning

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