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Dr. Kantarcioglu's research focuses on creating technologies that can efficiently extract useful information from any data without sacrificing privacy or security.
Security and privacy issues raised by data mining
Privacy issues in social networks
Security issues in databases
Privacy issues in health care
Use of data mining for fraud detection and homeland security
The Publications section lists any and all publications worked on. Research Explorer's search engine indexes data in this field for keyword searches. The category field is a user defined field where any number of categories can be created by the user to categorize publications. For example, publications can be categorized by the Journal that they appear in. Please click here for available slides.
Elizabeth Durham, Y. Xue, Murat Kantarcioglu, Bradley Malin, "Quantifying the Correctness, Computational Compexity, and Security of Privacy-preserving String comparators for Record Linkage". Information Fusion, to appear.
Robert Nix, Murat Kantarcioglu, "Incentive Compatible Privacy-Preserving Distributed Classification", IEEE Transactions on Depandable and Secure Computing, to appear. (pdf version)
Bijit Hore, Sharad Mehrotra, Mustafa Canim, Murat Kantarcioglu, "Secure Multidimensional Range Queries over Outsourced data", The VLDB Journal, VLDB Endowment, to appear. (pdf version)
Murat Kantarcioglu, Wei Jiang, “Incentive Compatible Privacy-Preserving Data Analysis”, IEEE Transactions on Knowledge and Data Engineering, to appear.
Tyrone Cadenhead, Murat Kantarcioglu, and Bhavani Thuraisingham, "A Framework for Policies over Provenance",3rd USENIX Workshop on the Theory and Practice of Provenance
Presentations and Projects
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Privacy-Preserving Distributed K-nn Classifier Privacy-Preserving Distributed K-nn Classifier, European Conf. on Principles of Data Mining and Knowledge Discovery (PKDD '04) 2004.
Privacy-preserving data integration and sharing Privacy-preserving data integration and sharing, The ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge (DMKD'2004) 2004.
Adversarial Learning Adversarial Learning at Rutgers University, March, 2006
Privacy-preserving Distributed Data Mining Privacy-preserving Distributed Data Mining, at the State University of New York at Buffalo, March, 2005
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UT Dallas computer scientist Murat Kantarcioglu has received a $400,000 award from the National Science Foundation Early Career Development Program, which is a highly selective program for junior faculty who are considered likely to become leaders in their field.
The award will fund a five-year effort to develop privacy-preserving technologies that could open the door to the widespread use of e-health and e-government applications.
“The CAREER program is one of the most competitive programs at NSF, and Murat’s award is a recognition of the excellence of his research and his potential to become a top national researcher,” said Mark Spong, dean of the Erik Jonsson School of Engineering and Computer Science and holder of the Lars Magnus Ericsson Chair in Electrical Engineering.
Project Seeks to Safeguard Privacy of Genomics Data
A new $1 million grant is intended to help ensure the privacy of each person whose information is included in increasingly vast banks of genomics data, according to a UT Dallas computer scientist.
The greatest potential for genomics-related medical advances lies in analyzing enormous combined stores of genomics data that may have originated from dozens of institutions and research studies, according to Dr. Murat Kantarcioglu, an assistant professor of computer science at UT Dallas. Such work could ultimately enable health care to be personalized to each patient’s genome.
“But the availability of such databanks for widespread use is contingent on protecting the anonymity of the individuals who correspond to the shared records,” said Kantarcioglu, co-investigator on the project based at Vanderbilt University. “Though policy and technical approaches for biomedical records privacy exist, they are inappropriate for environments that consolidate records from multiple organizations.”
Prof Warns of Risks Hiding on Social Network Sites
The data that can be easily extracted from people’s online social networking activities could be either a blessing or a curse, says a UT Dallas researcher.
On the one hand, an analysis of people’s interactions could improve public policy, helping city planners, for example, determine optimal locations for public health clinics. But on the other hand, you could have your identity stolen and your savings account wiped out after sharing seemingly innocuous details about yourself.
These are the sorts of things Dr. Murat Kantarcioglu is exploring.
In the early stages of his research, he’s asking questions such as whether details of your Facebook user profile and friendship links can be used to accurately predict your political affiliation. (Yes, according to his results.) Another question is whether a prospective employer could use your information to try to predict whether you would make a good employee.
The Additional Information section describes any other topics you wish to display on your profile that is not in another section. Research Explorer's search engine indexes data in this field for keyword searches. Please click here for available slides.
2005 Diamond Award for Outstanding Academic Achievement, Purdue University's Center for Education and Research in Information Assurance and Security (CERIAS)
2005-Present Assistant Professor of Computer Science (Tenure Track), University of Texas at Dallas
2001-2005 Research Assistant, Department of Computer Sciences, Purdue University
Summer 2004 Research Intern, IBM Almaden Research Labs (Mentor: Rakesh Agrawal)
Summer 2003 Research Intern, NEC C&C Research Labs (Mentor: Wen-syan Li)
Summer 2002 Research Intern, NEC C&C Research Labs (Mentor: Wen-syan Li)
2000-2001 Teaching Assistant, Department of Computer Sciences, Purdue University
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