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Big Structural/Graph/Complex Data De-anonymization: Practice, Quantification, and Implications

Shouling ji
Georgia Institute of Technology
Thursday, January 22, 2015 - 12:00pm to 1:00pm
Capital Suite, room 220

Nowadays, a large amount of data generated by computer networks and services have a graph structure, e.g., social graph data (Facebook, Google+, Twitter), which is referred to as structural/graph/complex data. Since these structural data have huge commercial value to businesses and potentially significant impacts to society, the security and privacy issues that arise during data release to the public, sharing with commercial partners, and/or transferring to third parties are attracting increasing interest. In this talk, I will introduce emerging structure-based de-anonymization attacks to structural/graph data and show how they break users’ privacy. Subsequently, I will introduce the theoretical foundation for the success of existing structure-based de-anonymization attacks and the de-anonymization quantification results of a large-scale evaluation leveraging 60+ real-world datasets, e.g., social network data, collaboration data, and mobility traces. Finally, I will discuss security implications.


About the Speaker: Shouling Ji is currently pursuing his second Ph.D. in the School of Electrical and Computer Engineering at Georgia Tech. He received his first Ph.D. degree with emphasis on wireless networking from the Department of Computer Science at Georgia State University. His current research interests include big data security and privacy, big data analytics, passwords, social networks, and wireless networking.