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

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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.
OriginalspråkEngelska
Titel på gästpublikationProceedings of ELM-2015
Antal sidor13
Volym2
UtgivningsortCham
FörlagSpringer
Utgivningsdatum03.01.2016
Sidor371-383
ISBN (tryckt)978-3-319-28372-2
ISBN (elektroniskt)978-3-319-28373-9
DOI
StatusPublicerad - 03.01.2016
MoE-publikationstypA4 Artikel i en konferenspublikation

Publikationsserier

Namn Proceedings in Adaptation, Learning and Optimization (PALO)
Volym7

Nyckelord

  • 512 Företagsekonomi

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