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Seminar abstract

Privacy-Preserving Social Network Analysis

Xintao Wu
Associate Professor
Department of Software and Information Systems at the University of North Carolina at Charlotte, USA


Abstract : Social networks often contain some private attribute information about individuals as well as their sensitive relationships. The privacy concerns associated with data analysis over social networks have incurred the recent research on privacy-preserving social network analysis, particularly on privacy-preserving publishing and querying social network data. Compared with well studied privacy-preservation techniques for tabular data, it is more challenging to design effective anonymization and perturbation techniques for publishing and mining social network data because of the difficulties in modeling link structures in social networks. In this talk, we survey the very recent research development on privacy-preserving publishing and analyzing social network data. In the first part, we focus on privacy preserving publishing social network data approaches: K-anonymity based privacy preservation via edge modification, probabilistic privacy preservation via edge randomization, and privacy preservation via generalization. In the second part, we focus on privacy preserving querying social network data and present the very recent private query answering techniques based on differential privacy. We finally discuss challenges and propose new research directions in this area.

Bio: Dr. Xintao Wu is an Associate Professor in the Department of Software and Information Systems at the University of North Carolina at Charlotte, USA. He got his Ph.D. degree in Information Technology from George Mason University in 2001. He received his BS degree in Information Science from the University of Science and Technology of China in 1994, an ME degree in Computer Engineering from the Chinese Academy of Space Technology in 1997. His major research interests include data mining, data privacy and security, and social network analysis. His recent research work has been to develop privacy preserving data mining techniques for both categorical and numerical data in traditional databases and linked data in social networks. Dr. Wu is an editor of Journal of Intelligent Information Systems, Transaction on Data Privacy, and International Journal of Social Network Mining. He was the program co-chair of the 2nd International Symposium on Data, Privacy and E-Commerce and served on program committees of top international conferences, including ACM KDD, IEEE ICDM, SIAM SDM, PKDD, and PAKDD. Dr. Wu is a recipient of NSF Career Award and college's Faculty Research Award from UNC Charlotte.
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