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Entropy 2019, 21(4), 334; https://doi.org/10.3390/e21040334

Using Minimum Local Distortion to Hide Decision Tree Rules

School of Science and Technology, Hellenic Open University, 263 35 Patras, Greece
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Received: 14 February 2019 / Revised: 20 March 2019 / Accepted: 25 March 2019 / Published: 28 March 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
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Abstract

The sharing of data among organizations has become an increasingly common procedure in several areas like banking, electronic commerce, advertising, marketing, health, and insurance sectors. However, any organization will most likely try to keep some patterns hidden once it shares its datasets with others. This article focuses on preserving the privacy of sensitive patterns when inducing decision trees. We propose a heuristic approach that can be used to hide a certain rule which can be inferred from the derivation of a binary decision tree. This hiding method is preferred over other heuristic solutions like output perturbation or cryptographic techniques—which limit the usability of the data—since the raw data itself is readily available for public use. This method can be used to hide decision tree rules with a minimum impact on all other rules derived. View Full-Text
Keywords: decision trees; privacy preserving; hiding rules; entropy; information gain; data sharing decision trees; privacy preserving; hiding rules; entropy; information gain; data sharing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Feretzakis, G.; Kalles, D.; Verykios, V.S. Using Minimum Local Distortion to Hide Decision Tree Rules. Entropy 2019, 21, 334.

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