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Large-Scale Scene Recognition Based on Extreme Learning Machines Read chapter Read first chapter

2016 | OriginalPaper | Chapter

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

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

Published in: Proceedings of ELM-2015 Volume 2

Publisher: Springer International Publishing

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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.

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previous chapter ELMVIS+: Improved Nonlinear Visualization Technique Using Cosine Distance and Extreme Learning Machines
next chapter Probabilistic Methods for Multiclass Classification Problems
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Metadata
Title
On Mutual Information over Non-Euclidean Spaces, Data Mining and Data Privacy Levels
Authors
Yoan Miche
Ian Oliver
Silke Holtmanns
Anton Akusok
Amaury Lendasse
Kaj-Mikael Björk
Copyright Year
2016
Book
Proceedings of ELM-2015 Volume 2

Print ISBN: 978-3-319-28372-2

Electronic ISBN: 978-3-319-28373-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-28373-9_32

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