A Technique to Improve De-anonymization Attacks on Graph Data

Javad Aliakbari, Mahshid Delavar, Javad Mohajeri, Mahmoud Salmasizadeh

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

Abstract / Description of output

Social networks and the shared data in these networks are always considered as good opportunities in hands of the attackers. To evaluate the privacy risks in these networks and challenge the anonymization techniques, several de-anonymization attacks have been introduced so far. In this paper, we propose a technique to improve the success rate of passive seed based de-anonymization attacks. Our proposed technique is simple and can be applied in combination with different types of de-anonymization attacks. We show that it can achieve high success rates with low number of seeds compared to similar attacks. Our technique can also be used for applying partial attacks on graphs which results in high success rate. We show the result of applying our technique on one of the best passive seed-based de-anonymization attacks introduced by Ji et al. The results prove our claims.
Original languageEnglish
Title of host publicationThe 26th Iranian Conference on Electrical Engineering (ICEE)
Place of PublicationMashhad, Iran
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)978-1-5386-4916-9
ISBN (Print)978-1-5386-4914-5
Publication statusPublished - 27 Sept 2018
Event26th Iranian Conference on Electrical Engineering - Sadjad University of Technology, Mashhad, Iran, Islamic Republic of
Duration: 8 May 201810 May 2018


Conference26th Iranian Conference on Electrical Engineering
Abbreviated titleICEE 2018
Country/TerritoryIran, Islamic Republic of
Internet address


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