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.
Recommended Citation
Y. Miche et al., "Practical Estimation of Mutual Information on Non-Euclidean Spaces," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10410 LNCS, pp. 123 - 136, Springer, Jan 2017.
The definitive version is available at https://doi.org/10.1007/978-3-319-66808-6_9
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-331966807-9
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2017
