Privacy in Big Data: Thinking Outside the Anonymity/Confidentiality Box
Chris Clifton - Purdue University
Jan 20, 2016Size: 151.5MB
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AbstractThe computer science community has had a growing research focus in Privacy over the last decade. Much of this has really focused on confidentiality: Anonymization, computing on encrypted data, access control policy, etc. This talk will look at a variety of research results in this area, including “weaker” approaches than the absolutes typically considered in the security community, and how they all come down to the same basic concept of providing confidentiality.
Privacy is much more complex. People are often willing to allow use of their data – but not just for anything. This talk will look at such other privacy issues, such as harm to individuals and society from the fear of disclosure or misuse of private data. The talk will conclude with ideas for new research directions in privacy.
About the SpeakerDr. 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.
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.
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