Practical Estimation of Mutual Information on Non-Euclidean Spaces

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10410)


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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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  1. 1.Nokia Bell LabsEspooFinland
  2. 2.Department of Mechanical and Industrial Engineering and the Iowa Informatics InitiativeThe University of IowaIowa CityUSA
  3. 3.Arcada University of Applied SciencesHelsinkiFinland

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