There have been significant research developments in technology to protect privacy. Unfortunately, few of these have made the transition to practice. A large part of the problem is the lack of an accepted way to measure privacy. Legal and regulatory terms do not translate well into technological solutions, and the plethora of technical approaches do not seem to resonate with privacy advocates.
This talk will discuss issues and challenges, with examples of the reason why a clear standard is difficult. A risk-based approach will be presented that allows anonymization based on controlling the potential damage from disclosure. This approach will be compared with more traditional anonymization measures, showing the difficulty of measuring the potential for harm from those measures.
This represents joint work with Mehmet Ercan Nergiz (Purdue University) and Maurizio Atzori (University of Pisa).
Dr. Clifton works on challenges posed by novel uses of data mining technology, including privacy-preserving data mining, 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 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. Before joining MITRE in 1995, he was an assistant professor of computer science at Northwestern University. He obtained his Ph.D. in Computer Science from Princeton University in 1991, and Bachelor's and Master's from the Massachusetts Institute of Technology in 1986.