Privacy -Enhancing Data Mining: Issues, Techniques and Measures
Li, Jingquan
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https://hdl.handle.net/2142/84538
Description
Title
Privacy -Enhancing Data Mining: Issues, Techniques and Measures
Author(s)
Li, Jingquan
Issue Date
2004
Doctoral Committee Chair(s)
Shaw, Michael J.
Department of Study
Business Administration
Discipline
Business Administration
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Business Administration, Management
Language
eng
Abstract
The study presents some effective privacy-enhancing transformation techniques that are applicable to various data types. The techniques are able to retain privacy while accessing the information contained in the original data. Specifically, we address the issue of privacy protection through using the data filter, partitioning, synthetic data, and randomization methods. We give examples of inducing the decision-tree classifiers and building detection models of fraud from training data in which the values of sensitive attribute values have been modified. We experimentally validate the privacy-enhancing techniques and the measurement methodology over both real world and synthetic datasets. The experimental results show that the application of privacy-enhancing techniques can preserve the data privacy with minimum loss of information. The results also demonstrate that the proposed techniques can achieve comparative performance measures or mining results while preserving the data privacy.
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