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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