Geometric Data Transformation For Privacy Preserving Clustering

• Author(s) / Creator(s)

  • Oliveira, Stanley
  • Zaiane, Osmar

• 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. | TRID-ID TR03-12

• Date created

  2003

• Subjects / Keywords

  • Privacy and Data Mining
  • Database Systems
  • Privacy Preserving Clustering
  • Geometric Data Transformation

• Type of Item

  Report

• DOI

  https://doi.org/10.7939/R3VX0686W

• License

  Attribution 3.0 International