Christopher W. Clifton
Professor of Computer Science
Interim Department Head
Joined department: Fall 2001
Dr. Clifton works on data privacy, particularly with respect to analysis of private data. This includes privacy-preserving data mining, data de-identification and anonymization, and limits on identifying individuals from data mining models. He also works more broadly in data mining, including data mining of text and data mining techniques applied to interoperation of heterogeneous information sources. 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 issues related to data privacy.
From September 2013-December 2015, he served as a Program Director at the National Science Foundation in the Information and Integration and Informatics cluster, also working with the Secure and Trustworthy Cyberspace Program. Prior to joining Purdue, Dr. Clifton was a principal scientist in the Information Technology Division at the MITRE Corporation. Before joining MITRE in 1995, he was an assistant professor of computer science at Northwestern University.
Jaideep Vaidya, Chris Clifton, and Michael Zhu, "Privacy Preserving Data Mining", Volume 19 in Advances in Information Security, Springer, New York, 2006, (url)
Mummoorthy Murugesan, Wei Jiang, Chris Clifton, Luo Si and Jaideep Vaidya, "Efficient Privacy-Preserving Similar Document Detection", The VLDB Journal, 19(4):457-475, August 2010. (url)
M. Ercan Nergiz and Chris Clifton, "δ-Presence Without Complete World Knowledge", IEEE Transactions on Knowledge and Data Engineering, 22(6):868-883, IEEE Computer Society, June 2010. (url)
Koray Mancuhan and Chris Clifton, "Combating Discrimination Using Bayesian Networks", Artificial Intelligence and Law 22(2):211-238 special issue on Computational Methods for Enforcing Privacy and Fairness, Sergio Mascetti, Annarita Ricci, and Salvatore Ruggieri, eds., June 2014.
Christine Task and Chris Clifton, "Differentially Private Significance Testing on Paired-Sample Data", 2016 SIAM International Conference on Data Mining (SDM16), Miami, Florida, May 5-7, 2016, pp. 153-161.