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Permanent link (DOI): https://doi.org/10.7939/R3VX0686W

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Geometric Data Transformation For Privacy Preserving Clustering Open Access

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Author or creator
Oliveira, Stanley
Zaiane, Osmar
Additional contributors
Subject/Keyword
Database Systems
Privacy and Data Mining
Geometric Data Transformation
Privacy Preserving Clustering
Type of item
Computing Science Technical Report
Computing science technical report ID
TR03-12
Language
English
Place
Time
Description
Technical report TR03-12. Despite its benefit in a wide range of applications, data mining techniques also have raised a number of ethical issues. Some such issues include those of privacy, data security, intellectual property rights, and many others. In this paper, we address the privacy problem against unauthorized secondary use of information. To do so, we introduce a family of geometric data transformation methods (GDTMs) which ensure that the mining process will not violate privacy up to a certain degree of security. We focus primarily on privacy preserving data clustering, notably on partition-based and hierarchical methods. Our proposed methods distort only confidential numerical attributes to meet privacy requirements, while preserving general features for clustering analysis. Our experiments demonstrate that our methods are effective and provide acceptable values in practice for balancing privacy and accuracy. We report the main results of our performance evaluation and discuss some open research issues.
Date created
2003
DOI
doi:10.7939/R3VX0686W
License information
Creative Commons Attribution 3.0 Unported
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