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Practical Estimation of Mutual Information on Non-Euclidean Spaces

Abstract : 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.
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Submitted on : Monday, January 8, 2018 - 9:49:44 AM
Last modification on : Monday, September 21, 2020 - 4:54:02 PM
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Yoan Miche, Ian Oliver, Wei Ren, Silke Holtmanns, Anton Akusok, et al.. Practical Estimation of Mutual Information on Non-Euclidean Spaces. 1st International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2017, Reggio, Italy. pp.123-136, ⟨10.1007/978-3-319-66808-6_9⟩. ⟨hal-01677135⟩



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