AbstractsComputer Science

Privacy-preserving data mining

by Nan Zhang

Institution: Texas A&M University
Year: 2009
Keywords: Data Mining
Record ID: 1853595
Full text PDF: http://hdl.handle.net/1969.1/ETD-TAMU-1080


In the research of privacy-preserving data mining, we address issues related to extracting knowledge from large amounts of data without violating the privacy of the data owners. In this study, we first introduce an integrated baseline architecture, design principles, and implementation techniques for privacy-preserving data mining systems. We then discuss the key components of privacy-preserving data mining systems which include three protocols: data collection, inference control, and information sharing. We present and compare strategies for realizing these protocols. Theoretical analysis and experimental evaluation show that our protocols can generate accurate data mining models while protecting the privacy of the data being mined.