Role-Based Access Control Models
Computer
An Object-Oriented RBAC Model for Distributed System
WICSA '01 Proceedings of the Working IEEE/IFIP Conference on Software Architecture
How much is "enough"? Risk in Trust-Based Access Control
WETICE '03 Proceedings of the Twelfth International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises
Using trust and risk in role-based access control policies
Proceedings of the ninth ACM symposium on Access control models and technologies
Trust for ubiquitous, transparent collaboration
Wireless Networks - Special issue: Pervasive computing and communications
Proceedings of the tenth ACM symposium on Access control models and technologies
Intrusion Detection in RBAC-administered Databases
ACSAC '05 Proceedings of the 21st Annual Computer Security Applications Conference
RoleMiner: mining roles using subset enumeration
Proceedings of the 13th ACM conference on Computer and communications security
Data Mining
Using Trust for Secure Collaboration in Uncertain Environments
IEEE Pervasive Computing
Trusting collaboration in global computing systems
iTrust'03 Proceedings of the 1st international conference on Trust management
Risk models for trust-based access Control(TBAC)
iTrust'05 Proceedings of the Third international conference on Trust Management
Cloud-Centric assured information sharing
PAISI'12 Proceedings of the 2012 Pacific Asia conference on Intelligence and Security Informatics
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Role Based Access Control (RBAC) has been introduced in an effort to facilitate authorization in database systems. It introduces roles as a new layer in between users and permissions. This not only provides a well maintained access granting mechanism, but also alleviates the burden to manage multiple users. While providing comprehensive access control, current RBAC models and systems do not take into consideration the possible risks that can be incurred with role misuse. In distributed environments a large number of users are a very common case, and a considerable number of them are first time users. This fact magnifies the need to measure risk before and after granting an access. We investigate the means of managing risks in RBAC employed distributed environments and introduce a probability based novel risk model. Based on each role, we use information about user credentials, current user queries, role history log and expected utility to calculate the overall risk. By executing data mining on query logs, our scheme generates normal query clusters. It then assigns different risk levels to individual queries, depending on how far they are from the normal clusters. We employ three types of granularity to represent queries in our architecture. We present experimental results on real data sets and compare the performances of the three granularity levels.