# Data set operations to hide decision tree rules

arXiv: https://arxiv.org/abs/1706.05733 · HTML: https://mlsanity.com/paper/1706.05733/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2017.

## Built on by (most cited first, 1 of 1)

- [Governance by Evidence: Regulated Predictors in Decision-Tree Models](https://mlsanity.com/paper/2512.15955/) (2025)

## Abstract (arXiv)

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.