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Efficient IRM enforcement of history-based access control policies
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ASIAN ACM Symposium on Information, Computer and Communications Security archive
Proceedings of the 4th International Symposium on Information, Computer, and Communications Security table of contents
Sydney, Australia
SESSION: Software security table of contents
Pages 35-46  
Year of Publication: 2009
ISBN:978-1-60558-394-5
Authors
Fei Yan  University of Regina Regina, Saskatchewan, Canada
Philip W. L. Fong  University of Regina Regina, Saskatchewan, Canada
Sponsor
SIGSAC: ACM Special Interest Group on Security, Audit, and Control
Publisher
ACM  New York, NY, USA
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ABSTRACT

Inlined Reference Monitor (IRM) is an established enforcement mechanism for history-based access control policies. IRM enforcement injects monitoring code into the binary of an untrusted program in order to track its execution history. The injected code denies access when execution deviates from the policy. The viability of IRM enforcement is predicated on the ability of the binary rewriting element to optimize away redundant monitoring code without compromising security.

This work proposes a novel optimization framework for IRM enforcement. The scheme is based on a constrained representation of history-based access control policies, which, despite its constrained expressiveness, can express such policies as separation of duty, generalized Chinese Wall policies, and hierarchical one-out-of-k authorization. An IRM optimization procedure has been designed to exploit the structure of this policy representation. The optimization scheme is then extended into a distributed optimization protocol, in which an untrusted code producer attempts to help boost the optimization effectiveness of an IRM enforcement mechanism administered by a distrusting code consumer. It is shown that the optimization procedure provably preserves security even in the midst of distributed optimization. A prototype of the optimization procedure has been implemented for Java bytecode, and its effectiveness has been empirically profiled.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Fei Yan: colleagues
Philip W. L. Fong: colleagues