%0 Conference Proceedings
%T Practical Estimation of Mutual Information on Non-Euclidean Spaces
%+ Nokia Bell Labs [Espoo]
%+ Arcada University of Applied Sciences
%+ University of Iowa [Iowa City]
%A Miche, Yoan
%A Oliver, Ian
%A Ren, Wei
%A Holtmanns, Silke
%A Akusok, Anton
%A Lendasse, Amaury
%Z Part 3: MAKE Privacy
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 1st International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Reggio, Italy
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-10410
%P 123-136
%8 2017-08-29
%D 2017
%R 10.1007/978-3-319-66808-6_9
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X We propose, in this paper, to address the issue of measuring the impact of privacy and anonymization techniques, by measuring the data loss between “before” and “after”. The proposed approach focuses therefore on data usability, more than in ensuring that the data is sufficiently anonymized. We use Mutual Information as the measure criterion for this approach, and detail how we propose to measure Mutual Information over non-Euclidean data, in practice, using two possible existing estimators. We test this approach using toy data to illustrate the effects of some well known anonymization techniques on the proposed measure.
%G English
%Z TC 5
%Z TC 8
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-01677135/document
%2 https://inria.hal.science/hal-01677135/file/456304_1_En_9_Chapter.pdf
%L hal-01677135
%U https://inria.hal.science/hal-01677135
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-TC8
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-LNCS-10410
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9