Evaluating the security of anonymized big graph/structural data

Evaluating the security of anonymized big graph/structural data

We studied the security of anonymized big graph data. Our main contributions include: new De-Anonymization (DA) attacks, comprehensive anonymity, utility, and de-anonymizability quantifications, and a secure graph data publishing/sharing system SecGraph. New DA Attacks. We present two novel graph DA...

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Bibliographic Details
Main Author: Ji, Shouling
Other Authors: Bloch, Matthieu
Format: Others
Language:en_US
Published: Georgia Institute of Technology 2016
Subjects:
Security and privacy
Graph data
Structural data
De-anonymization
Anonymization
Quantification
Evaluation
Utility
Anonymity
Online Access:http://hdl.handle.net/1853/54913

Internet

http://hdl.handle.net/1853/54913

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