Professor of Computer Science; Professor of Statistics (courtesy) 

• Computer Sciences

LWSN 2142F 

(765) 494-6005 

clifton@purdue.edu 

https://www.cs.purdue.edu/people/faculty/clifton.html 

https://www.cs.purdue.edu/homes/clifton/ 

B.S. and M.S. from the Massachusetts Institute of Technology, and M.A. and Ph.D. from Princeton University 

Assistant Professor of Computer Science at Northwestern University. 

Privacy Issues in Information Management, Data Mining, Data Security, Database Support for Text, and Heterogeneous Databases. 

Assurable Software and Architecture Incident Detection, Response, Investigation Identification, Authentication, Privacy 

Principal Scientist for the MITRE Corporation. Works on challenges posed by novel uses of data mining technology, including data mining of text, data mining techniques applied to interoperation of heterogeneous information sources, and security and privacy issues raised by data mining. Fundamental data mining challenges posed by these applications include extracting knowledge from noisy data, identifying knowledge in highly skewed data (few examples of "interesting" behavior), and limits on learning. Also works on database support for widely distributed and autonomously controlled information, particularly information administration issues such as supporting fine-grained access control. 

TechPoint Mira Award [2005]
IEEE ICDM Outstanding Service Award [2011]

Teaching for Tomorrow Fellowship [2011]

Distinguished Member, Association for Computing Machinery (ACM) [2017]

Fellow, Institute of Electronic and Electrical Engineers (IEEE) [2020]

ACM CODASPY Research Award [2020] 

Secure Set Intersection Cardinality with Application to Association Rule Mining (with J. Vaidya), Journal of Computer Security (to appear). Privacy-Preserving Data Mining: Why, How, and What For? (with J. Vaidya), IEEE Security & Privacy (November/December, 2004). Privacy Preserving Data Mining of Association Rules on Horizontally Partitioned Data (with M. Kantarcioglu), Transactions on Knowledge and Data Engineering 16(9), IEEE Computer Society Press (September 2004). 

Chris Clifton is an Associate Professor of Computer Science at Purdue University. He has a Ph.D. from Princeton University, and Bachelor's and Master's degrees from the Massachusetts Institute of Technology. Prior to joining Purdue in 2001, Chris had served as a Principal Scientist at The MITRE Corporation and as an Assistant Professor of Computer Science at Northwestern University. His research interests include data mining, data security, database support for text, and heterogeneous databases. Dr. Clifton works on challenges posed by novel uses of data mining technology, including data mining of text, data mining techniques applied to interoperation of heterogeneous information sources, and security and privacy issues raised by data mining. Fundamental data mining challenges posed by these applications, include extracting knowledge from noisy data, identifying knowledge in highly skewed data (few examples of "interesting" behavior), and limits on learning. He also works on database support for widely distributed and autonomously controlled information, particularly information administration issues such as supporting fine-grained access control. Prior to joining Purdue, Dr. Clifton was a Principal Scientist in the Information Technology Division at the MITRE Corporation. He has a Ph.D. from Princeton University, and Bachelor's and Master's degrees from the Massachusetts Institute of Technology. Before joining MITRE in 1995, he was an Assistant Professor of Computer Science at Northwestern University.