Aya Mohamed, Dagmar Auer, Daniel Hofer, Josef Küng,
"Extended XACML Language and Architecture for Access Control in Graph-structured Data"
: The 23rd International Conference on Information Integration and Web Intelligence (iiWAS2021), Serie International Conference on Information Integration and Web Intelligence, ACM, New York, USA, 2021, ISSN: 2662-995X
Original Titel:
Extended XACML Language and Architecture for Access Control in Graph-structured Data
Sprache des Titels:
Englisch
Original Buchtitel:
The 23rd International Conference on Information Integration and Web Intelligence (iiWAS2021)
Original Kurzfassung:
The rapidly increasing use of graph databases for a wide variety of applications demands flexible authorization and fine-grained access control at the level of attributes associated with the basic entities (i.e., accessing subject, requested resource, performed action, and environmental conditions) but also the vertices and edges along a particular access path. We present a solution for authorization policy specification and enforcement in a graph database to apply fine-grained path-specific constraints on graph-structured data. Therefore, we extend the well-established declarative policy definition language eXtensible Access Control Markup Language (XACML) and its architecture to describe path patterns and enforce the policies using the standard functional components of XACML. Our approach, XACML for Graph-structured data (XACML4G), defines an extended XACML grammar for the authorization policy and access request. To enforce XACML4G policies, we relied on the extensibility points of the XACML architecture and added proprietary extensions. We show the significance of our approach by means of a demonstration prototype in the university domain. Finally, we provide an initial evaluation of the expressiveness and performance of XACML4G with regard to XACML.
Sprache der Kurzfassung:
Englisch
Veröffentlicher:
ACM
Verlagsanschrift:
New York, USA
Serie:
International Conference on Information Integration and Web Intelligence
Forschungs-