Data set operations to hide decision tree rules

TL;DR

This paper presents a record augmentation method to hide sensitive decision tree rules in binary datasets, enhancing privacy while maintaining data usability, demonstrated through key lemmas, an example, and a prototype experiment.

Contribution

It introduces a novel record augmentation approach for privacy-preserving rule hiding in decision trees, outperforming traditional heuristic and cryptographic methods.

Findings

The method effectively hides sensitive rules in datasets.

The approach preserves data usability for public analysis.

Experimental results validate the effectiveness of the hiding technique.

Abstract

This paper focuses on preserving the privacy of sensitive patterns when inducing decision trees. We adopt a record augmentation approach for hiding sensitive classification rules in binary datasets. Such a hiding methodology is preferred over other heuristic solutions like output perturbation or cryptographic techniques - which restrict the usability of the data - since the raw data itself is readily available for public use. We show some key lemmas which are related to the hiding process and we also demonstrate the methodology with an example and an indicative experiment using a prototype hiding tool.