Enhancing De-anonymization Attacks on Graph Data, M.Sc. Thesis Sharif University of Technology ; Mohajeri, Javad (Supervisor) ; Salmasizadeh, Mahmoud (Co-Supervisor)
Abstract
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 thesis, 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...
Cataloging briefEnhancing De-anonymization Attacks on Graph Data, M.Sc. Thesis Sharif University of Technology ; Mohajeri, Javad (Supervisor) ; Salmasizadeh, Mahmoud (Co-Supervisor)
Abstract
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 thesis, 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...
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