2018
DOI: 10.1007/978-3-030-02450-5_17
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Using Theorem Provers to Increase the Precision of Dependence Analysis for Information Flow Control

Abstract: Information flow control (IFC) is a category of techniques for enforcing information flow properties. In this paper we present the Combined Approach, a novel IFC technique that combines a scalable system-dependence-graph-based (SDG-based) approach with a precise logic-based approach based on a theorem prover. The Combined Approach has an increased precision compared with the SDG-based approach on its own, without sacrificing its scalability. For every potential illegal information flow reported by the SDG-base… Show more

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Cited by 1 publication
(7 citation statements)
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“…This section presents the approaches in the Noninterference Framework. We present SDG-based approaches (using Herda et al [22]) in Section 3.1, logicbased approaches (using previous works [6,21]) in Section 3.2, and automatic test generation based on symbolic execution (using Herda et al [21]) in Section 3.3.…”
Section: Approaches Of the Noninterference Frameworkmentioning
confidence: 99%
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“…This section presents the approaches in the Noninterference Framework. We present SDG-based approaches (using Herda et al [22]) in Section 3.1, logicbased approaches (using previous works [6,21]) in Section 3.2, and automatic test generation based on symbolic execution (using Herda et al [21]) in Section 3.3.…”
Section: Approaches Of the Noninterference Frameworkmentioning
confidence: 99%
“…In this article, we present an overview of recent combinations [6,21,22] of deductive program verification with automatic test generation on the one hand and static analysis on the other hand, with the goal of checking noninterference. The resulting Noninterference Framework can be used both for proving that a given program fulfills a given noninterference property and, also, for finding a counterexample that demonstrates a noninterference violation in case the noninterference property is not fulfilled.…”
Section: Introductionmentioning
confidence: 99%
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