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On the Relations between Retention Replacement, Additive Perturbation, and Randomisations for Nominal Attributes in Privacy Preserving Data Mining

  • Piotr Andruszkiewicz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7661)

Abstract

There are several randomisation-based methods in Privacy Preserving Data Mining. In this paper we discuss the additive perturbation and the retention replacement for continuous attributes. We also investigate the randomisations for binary and nominal attributes. We focus on the relations between them, similarities, and differences. We also discuss properties of randomisation-based methods which are important in real applications during implementation and the usage of particular randomisations. We have proven that the retention replacement can be implemented with the randomisation for nominal attributes. We have also shown that the additive perturbation can be approximated with the aforementioned solution for nominal attributes.

Keywords

Probability Density Function Continuous Attribute Association Rule Mining Privacy Preserve Nominal Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Piotr Andruszkiewicz
    • 1
  1. 1.Institute of Computer ScienceWarsaw University of TechnologyPoland

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