Leveraging lattices to improve role mining

Alessandro Colantonio, Roberto Di Pietro, Alberto Ocello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations


In this paper we provide a new formal framework applicable to role mining algorithms. This framework is based on a rigorous analysis of identifiable patterns in access permission data. In particular, it is possible to derive a lattice of candidate roles from the permission powerset. We formally prove some interesting properties about such lattices. These properties, a contribution on their own, can be applied practically to optimize role mining algorithms. Data redundancies associated with co-occurrences of permissions among users can be easily identified and eliminated, allowing for increased output quality and reduced processing time. To prove the effectiveness of our proposal, we have applied our results to two existing role mining algorithms: Apriori and RBAM. Application of these modified algorithms to a realistic data set consistently reduced running time and, in some cases, also greatly improved output quality; all of which confirmed our analytical findings. © 2008 Springer Science+Business Media, LLC.
Original languageEnglish (US)
Title of host publicationIFIP International Federation for Information Processing
PublisherSpringer New York
Number of pages15
ISBN (Print)9780387096988
StatePublished - Jan 1 2008
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-20

ASJC Scopus subject areas

  • Information Systems and Management


Dive into the research topics of 'Leveraging lattices to improve role mining'. Together they form a unique fingerprint.

Cite this