On Using Linear Diophantine Equations to Tune the extent of Look Ahead while Hiding Decision Tree Rules

Georgios Feretzakis; Dimitris Kalles; Vassilios S. Verykios

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arXiv · 1710.07214

On Using Linear Diophantine Equations to Tune the extent of Look Ahead while Hiding Decision Tree Rules

Georgios Feretzakis·Dimitris Kalles·Vassilios S. Verykios

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Abstract

This paper focuses on preserving the privacy of sensitive pat-terns when inducing decision trees. We adopt a record aug-mentation approach for hiding sensitive classification rules in binary datasets. Such a hiding methodology is preferred over other heuristic solutions like output perturbation or crypto-graphic techniques - which restrict the usability of the data - since the raw data itself is readily available for public use. In this paper, we propose a look ahead approach using linear Diophantine equations in order to add the appropriate number of instances while minimally disturbing the initial entropy of the nodes.

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Georgios Feretzakis, Dimitris Kalles, Vassilios S. Verykios. 2017-10-18. On Using Linear Diophantine Equations to Tune the extent of Look Ahead while Hiding Decision Tree Rules. https://arxiv.org/abs/1710.07214

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