On the Simultaneous Preservation of Privacy and Data Analytic in Anonymized Networks

Negar Kiyavash (University of Illinois at Urbana-Champaign)

The proliferation of online social networks has helped in generating large amounts of graph data which has immense value for data analytics. Network operators, like Facebook, often share this data with researchers or third party organizations, which helps both the entities generate revenues and improve their services. As this data is shared with third party organizations, the concern of user privacy becomes pertinent. Hence, it becomes essential to balance utility and privacy while releasing such data. Advances in graph matching and the resulting recent attacks on graph datasets paints a grim picture. We discuss the feasibility of privacy preserving data analytics in anonymized networks and provide an answer to the question “Does there exist a regime where the network cannot be deanonymized, yet data analytics can be performed?

About Negar Kiyavash
Negar Kiyavash is Willett Faculty Scholar at the University of Illinois at Urbana- Champaign. She is a joint Associate Professor of Industrial and Enterprise Engineering and Electrical and Computer Engineering. She is also affiliated with the Coordinated Science Laboratory (CSL) and the Information Trust Institute. She received her Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana- Champaign in 2006. Her research interests are in design and analysis of algorithms for network inference and security. She is a recipient of National Science Foundation’s CAREER and The Air Force Office of Scientific Research Young Investigator awards, and the Illinois College of Engineering Dean’s Award for Excellence in Research.