Access Control Enforcement for Selective Disclosure of Linked Data

Abstract

The Semantic Web technologies enable Web-scaled data linking between large RDF repositories. However, it happens that organizations cannot publish their whole datasets but only some subsets of them, due to ethical, legal or confidentiality considerations. Different user profiles may have access to different authorized subsets. In this case, selective disclosure appears as a promising incentive for linked data. In this paper, we show that modular, fine-grained and efficient selective disclosure can be achieved on top of existing RDF stores. We use a data-annotation approach to enforce access control policies. Our results are grounded on previously established formal results proposed in [14]. We present an implementation of our ideas and we show that our solution for selective disclosure scales, is independent of the user query language, and incurs reasonable overhead at runtime.

This work is supported by Thomson Reuters in the framework of the Partner University Fund project: “Cybersecurity Collaboratory: Cyberspace Threat Identification, Analysis and Proactive Response”. The Partner University Fund is a program of the French Embassy in the United States and the FACE Foundation and is supported by American donors and the French government.