On Mutual Information over Non-Euclidean Spaces, Data Mining and Data Privacy Levels

Yoan Miche, Ian Oliver, Silke Holtmanns, Anton Akusok, Amaury Lendasse, Kaj-Mikael Björk

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

In this paper, we propose a framework for measuring the impact of data privacy techniques, in information theoretic and in data mining terms. The need for data privacy and anonymization is often hampered by the fact that the privacy functions alter the data in non-measurable amounts and details. We propose here to use Mutual Information over non-Euclidean spaces as a means of measuring this distortion. In addition, and following the same principle, we also propose to use Machine Learning techniques in order to quantify the impact of the data obfuscation in terms of further data mining goals.
Original languageEnglish
Title of host publicationProceedings of ELM-2015
Number of pages13
Volume2
Place of PublicationCham
PublisherSpringer
Publication date03.01.2016
Pages371-383
ISBN (Print)978-3-319-28372-2
ISBN (Electronic)978-3-319-28373-9
DOIs
Publication statusPublished - 03.01.2016
MoE publication typeA4 Article in conference proceedings

Publication series

Name Proceedings in Adaptation, Learning and Optimization (PALO)
Volume7

Keywords

  • 512 Business and Management
  • Mutual Information
  • Extreme Learn Machine
  • Machine Learn Technique
  • Data Privacy
  • Neighbour Graph

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