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Entropy 2019, 21(1), 66; https://doi.org/10.3390/e21010066

On Using Linear Diophantine Equations for in-Parallel Hiding of Decision Tree Rules

School of Science and Technology, Hellenic Open University, Patras 263 35,Greece
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Received: 10 December 2018 / Revised: 1 January 2019 / Accepted: 10 January 2019 / Published: 14 January 2019
(This article belongs to the Special Issue Entropy Based Inference and Optimization in Machine Learning)
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Abstract

Data sharing among organizations has become an increasingly common procedure in several areas such as advertising, marketing, electronic commerce, banking, and insurance sectors. However, any organization will most likely try to keep some patterns as hidden as possible once it shares its datasets with others. This paper focuses on preserving the privacy of sensitive patterns when inducing decision trees. We adopt a record augmentation approach to hide critical classification rules in binary datasets. Such a hiding methodology 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. We propose a look ahead technique using linear Diophantine equations to add the appropriate number of instances while maintaining the initial entropy of the nodes. This method can be used to hide one or more decision tree rules optimally.
Keywords: decision trees; privacy preserving; Diophantine equations; hiding rules; entropy; information gain; data sharing decision trees; privacy preserving; Diophantine equations; hiding rules; entropy; information gain; data sharing
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. On Using Linear Diophantine Equations for in-Parallel Hiding of Decision Tree Rules. Entropy 2019, 21, 66.

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