# Greedy and Evolutionary Algorithms for Mining Relationship-Based Access Control Policies

arXiv: https://arxiv.org/abs/1708.04749 · HTML: https://mlsanity.com/paper/1708.04749/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2017.

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

- [Machine Learning in Access Control: A Taxonomy and Survey](https://mlsanity.com/paper/2207.01739/) (2022)
- [Quantifying Policy Administration Cost in an Active Learning Framework](https://mlsanity.com/paper/2401.00086/) (2023)

## Abstract (arXiv)

Relationship-based access control (ReBAC) provides a high level of expressiveness and flexibility that promotes security and information sharing. We formulate ReBAC as an object-oriented extension of attribute-based access control (ABAC) in which relationships are expressed using fields that refer to other objects, and path expressions are used to follow chains of relationships between objects. ReBAC policy mining algorithms have potential to significantly reduce the cost of migration from legacy access control systems to ReBAC, by partially automating the development of a ReBAC policy from an existing access control policy and attribute data. This paper presents two algorithms for mining ReBAC policies from access control lists (ACLs) and attribute data represented as an object model: a greedy algorithm guided by heuristics, and a grammar-based evolutionary algorithm. An evaluation of the algorithms on four sample policies and two large case studies demonstrates their effectiveness.