# A Decision Tree Learning Approach for Mining Relationship-Based Access Control Policies

arXiv: https://arxiv.org/abs/1909.12095 · HTML: https://mlsanity.com/paper/1909.12095/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Toward Deep Learning Based Access Control](https://mlsanity.com/paper/2203.15124/) (2022)
- [Machine Learning in Access Control: A Taxonomy and Survey](https://mlsanity.com/paper/2207.01739/) (2022)

## Abstract (arXiv)

Relationship-based access control (ReBAC) provides a high level of expressiveness and flexibility that promotes security and information sharing, by allowing policies to be expressed in terms of chains of relationships between entities. ReBAC policy mining algorithms have the potential to significantly reduce the cost of migration from legacy access control systems to ReBAC, by partially automating the development of a ReBAC policy. This paper presents new algorithms, called DTRM (Decision Tree ReBAC Miner) and DTRM^-, based on decision trees, for mining ReBAC policies from access control lists (ACLs) and information about entities. Compared to state-of-the-art ReBAC mining algorithms, our algorithms are significantly faster, achieve comparable policy quality, and can mine policies in a richer language.