@inproceedings{314c9d726b4d481bbbdd18fd786c9066,
title = "Learning Attribute-Based and Relationship-Based Access Control Policies with Unknown Values",
abstract = "Attribute-Based Access Control (ABAC) and Relationship-based access control (ReBAC) provide a high level of expressiveness and flexibility that promote security and information sharing, by allowing policies to be expressed in terms of attributes of and chains of relationships between entities. Algorithms for learning ABAC and ReBAC policies from legacy access control information have the potential to significantly reduce the cost of migration to ABAC or ReBAC. This paper presents the first algorithms for mining ABAC and ReBAC policies from access control lists (ACLs) and incomplete information about entities, where the values of some attributes of some entities are unknown. We show that the core of this problem can be viewed as learning a concise three-valued logic formula from a set of labeled feature vectors containing unknowns, and we give the first algorithm (to the best of our knowledge) for that problem.",
author = "Thang Bui and Stoller, \{Scott D.\}",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th International Conference on Information Systems Security, ICISS 2020 ; Conference date: 16-12-2020 Through 20-12-2020",
year = "2020",
doi = "10.1007/978-3-030-65610-2\_2",
language = "English",
isbn = "9783030656096",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "23--44",
editor = "Salil Kanhere and Patil, \{Vishwas T\} and Shamik Sural and Gaur, \{Manoj S\}",
booktitle = "Information Systems Security - 16th International Conference, ICISS 2020, Proceedings",
}