Privacy -Enhancing Data Mining: Issues, Techniques and Measures

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Business Administration

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Li, Jingquan

Contributors dc:contributor

• Shaw, Michael J.

• Business Administration, Management

Language dc:language

eng

Identifier

(MiAaPQ)AAI3160914

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/84538