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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-1080 |
| Date | 15 May 2009 |
| Creators | Zhang, Nan |
| Contributors | Chen, Jianer, Zhao, Wei |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
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