@inproceedings{9011e0032a5b45f19a3c24939d06165e,
title = "On Mutual Information over Non-Euclidean Spaces, Data Mining and Data Privacy Levels",
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.",
keywords = "512 Business and Management, Mutual Information, Extreme Learn Machine, Machine Learn Technique, Data Privacy, Neighbour Graph",
author = "Yoan Miche and Ian Oliver and Silke Holtmanns and Anton Akusok and Amaury Lendasse and Kaj-Mikael Bj{\"o}rk",
year = "2016",
month = jan,
day = "3",
doi = "10.1007/978-3-319-28373-9_32",
language = "English",
isbn = "978-3-319-28372-2",
volume = "2",
series = " Proceedings in Adaptation, Learning and Optimization (PALO)",
publisher = "Springer",
pages = "371--383 ",
booktitle = "Proceedings of ELM-2015",
address = "International",
}