utd_bio_pic_06-22_18Name : Murat Kantarcioglu

Institution : University of Texas at Dallas

 

Murat Kantarcioglu is an assistant professor of computer science at University of Texas at Dallas. He had a Ph.D. degree from Purdue University in 2005. He received his master's in Computer Science from Purdue University in 2002 and his bachelor degree in computer engineering from METU, Ankara, Turkey in 2000. He is also a recipient of NSF CAREER Award.

 

His research focuses on creating technologies that efficiently extract useful information from any data without sacrificing privacy or security. His current research is funded by grants from NSF, AFOSR, ONR and IARPA.

 

 

Publications :

 

Ali Inan, Murat Kantarcioglu, Elisa Bertino,and Monica Scannapieco, "A Hybrid Approach to Private Record Linkage", 24th IEEE Int'l Conf.on Data Engineering, 2008

 

Murat Kantarcioglu, Wei Jiang, Ying Liu, and Bradley Malin, "A Cryptographic Approach to Securely Share and Query Genomic Sequences", IEEE Transactions on Information Technology in Biomedicine, Vol. 12, No. 5, pp 606-617 (2008)

 

 

Title of Project: Privacy Preserving Distributed Data Analytics

 

Increasingly, different organizations need to securely share their private data to execute many critical tasks. Recently, several different approaches based on secure multi-party computation (SMC) and data sanitization techniques have emerged to enable privacy preserving distributed data analytics. Although SMC based privacy-preserving protocols allow the participating parties to learn only the final (accurate) result, they do not scale well for large amounts of data. On the other hand, sanitization based techniques allow organizations to reveal privacy sensitive data under some privacy guarantees by distorting the data. In many cases, significant data distortion that is needed to preserve privacy could lead to inaccurate results.

 

Due to the limitations of the current approaches, efficient and accurate privacy-preserving solutions are needed for handling large distributed data sets. To address this challenge, we design and develop a novel framework where sanitization and SMC techniques are integrated to develop efficient privacy-preserving solutions under resource constraints. Basically, we use the data sanitization techniques to get initial approximate results and carry out SMC operations selectively to increase the accuracy. Since we use existing techniques in a black box fashion, our approach is orthogonal to any new sanitization or SMC techniques.

 

Our new techniques could substantially decrease the cost of executing privacy-preserving distributed data analytics protocols. This could have a direct economic impact by opening the way for new applications (e.g., e-health and e-government applications) that are at present considered infeasible due to the lack of necessary privacy-preserving solutions that can work efficiently on very large data sets.

 

In addition, we discuss the effect of incentives, disclosure risks and other economical issues in privacy-preserving data sharing and show that in some cases, misaligned incentives may hamper critical data sharing.