Sophisticated Access Control via SMT and Logical Frameworks

Abstract

We introduce a new methodology for formulating, analyzing, and applying access-control policies. Policies are expressed as formal theories in the SMT (satisfiability-modulo-theories) subset of typed first-order logic, and represented in a programmable logical framework, with each theory extending a core ontology of access control. We reduce both request evaluation and policy analysis to SMT solving, and provide experimental results demonstrating the practicality of these reductions. We also introduce a class of canonical requests and prove that such requests can be evaluated in linear time. In many application domains, access requests are either naturally canonical or can easily be put into canonical form. The resulting policy framework is more expressive than XACML and languages in the Datalog family, without compromising efficiency. Using the computational logic facilities of the framework, a wide range of sophisticated policy analyses (including consistency, coverage, observational equivalence, and change impact) receive succinct formulations whose correctness can be straightforwardly verified. The use of SMT solving allows us to efficiently analyze policies with complicated numeric (integer and real) constraints, a weak point of previous policy analysis systems. Further, by leveraging the programmability of the underlying logical framework, our system provides exceptionally flexible ways of resolving conflicts and composing policies. Specifically, we show that our system subsumes FIA (Fine-grained Integration Algebra), an algebra recently developed for the purpose of integrating complex policies.

Document Details

Document Type
Pub Defense Publication
Publication Date
Apr 01, 2014
Source ID
10.1145/2595222

Entities

People

  • Jason Chiang
  • Konstantine Arkoudas
  • Ritu Chadha

Organizations

  • United States Army

Tags

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Cybersecurity.
  • Mathematical Modeling and Probability Theory.