NDLI: Generative models for access control policies: applications to role mining over logs with attribution

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Generative models for access control policies: applications to role mining over logs with attribution

Content Provider	ACM Digital Library
Author	Molloy, Ian Park, Youngja Chari, Suresh
Abstract	We consider a fundamentally new approach to role and policy mining: finding RBAC models which reflect the observed usage of entitlements and the attributes of users. Such policies are interpretable, i.e., there is a natural explanation of why a role is assigned to a user and are conservative from a security standpoint since they are based on actual usage. Further, such "generative" models provide many other benefits including reconciliation with policies based on entitlements, detection of provisioning errors, as well as the detection of anomalous behavior. Our contributions include defining the fundamental problem as extensions of the well-known role mining problem, as well as providing several new algorithms based on generative machine learning models. Our algorithms find models which are causally associated with actual usage of entitlements and any arbitrary combination of user attributes when such information is available. This is the most natural process to provision roles, thus addressing a key usability issue with existing role mining algorithms. We have evaluated our approach on a large number of real life data sets, and our algorithms produce good role decompositions as measured by metrics such as coverage, stability, and generality We compare our algorithms with traditional role mining algorithms by equating usage with entitlement. Results show that our algorithms improve on existing approaches including exact mining, approximate mining, and probabilistic algorithms; the results are more temporally stable than exact mining approaches, and are faster than probabilistic algorithms while removing artificial constraints such as the number of roles assigned to each user. Most importantly, we believe that these roles more accurately capture what users actually do, the essence of a role, which is not captured by traditional methods.
Starting Page	45
Ending Page	56
Page Count	12
File Format	PDF
ISBN	9781450312950
DOI	10.1145/2295136.2295145
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-06-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Access control Rbac Role mining Generative models
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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