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.