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A decision support system for secure information sharing

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Published:03 June 2009Publication History

ABSTRACT

In both the commercial and defense sectors a compelling need is emerging for highly dynamic, yet risk optimized, sharing of information across traditional organizational boundaries. Risk optimal decisions to disseminate mission critical tactical intelligence information to the pertinent actors in a timely manner is critical for a mission's success. In this paper1, we argue that traditionally decision support mechanisms for information sharing (such as Multi-Level Security (MLS)) besides being rigid and situation agnostic, do not offer explanations and diagnostics for non-shareability. This paper exploits rich security metadata and semantic knowledgebase that captures domain specific concepts and relationships to build a logic for risk optimized information sharing. We show that the proposed approach is: (i) flexible: e.g., sensitivity of tactical information decays with space, time and external events, (ii) situation-aware: e.g., encodes need-to-know based access control policies, and more importantly (iii) supports explanations for non-shareability; these explanations in conjunction with rich security metadata and domain ontology allows a sender to intelligently transform information (e.g., downgrade information, say, by deleting participant list in a meeting) with the goal of making transformed information shareable with the recipient. In this paper, we will describe an architecture for secure information sharing using a publicly available hybrid semantic reasoner and present several illustrative examples that highlight the benefits of our proposal over traditional approaches.

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          cover image ACM Conferences
          SACMAT '09: Proceedings of the 14th ACM symposium on Access control models and technologies
          June 2009
          258 pages
          ISBN:9781605585376
          DOI:10.1145/1542207

          Copyright © 2009 ACM

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          Publication History

          • Published: 3 June 2009

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          SACMAT '09 Paper Acceptance Rate24of75submissions,32%Overall Acceptance Rate177of597submissions,30%

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