Algorithms for Model Checking HyperLTL and HyperCTL $$^*$$

Finkbeiner, Bernd; Rabe, Markus N.; Sánchez, César

doi:10.1007/978-3-319-21690-4_3

Cited by 159 publications

(286 citation statements)

References 33 publications

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“…We compare the Max#Sat-based approach to the expansionbased approach using HyperLTL [26]. Our implementation uses the MaxCount tool [29].…”

Section: Methodsmentioning

confidence: 99%

“…While several verification techniques for hyperproperties exists [5,31,38,42], the literature was missing general approaches to quantitative information-flow control. SecLTL [25] was introduced as first general approach to model check (quantitative) hyperproperties, before HyperLTL [18], and its corresponding model checker [26], was introduced as a temporal logic for hyperproperties, which subsumes the previous approaches.…”

Section: Related Workmentioning

confidence: 99%

“…In the best case, when ∈ {≤, <}, ψ ι does not contain existential quantifiers, and ψ does not contain universal quantifiers, we obtain an HyperLTL formula without quantifier alternations, where the number of quantifiers grows linearly with the bound n. For m quantifiers, the HyperLTL model checking algorithm [26] constructs and analyzes the m-fold self-composition of the Kripke structure. The running time of the model checking algorithm is thus exponential in the bound.…”

Section: Example 3 (Quantitative Non-interference In Hyperltl)mentioning

confidence: 99%

“…For example, observational determinism [47], the requirement that any pair of traces that have the same observable input also have the same observable output, can be expressed as the following HyperLTL formula: ∀π.∀π . ( π = I π ) → ( π = O π ) For many information flow policies of interest, including observational determinism, there is no longer a need for property-specific algorithms: it has been shown that the standard HyperLTL model checking algorithm [26] performs just as well as a specialized algorithm for the respective property.…”

Section: Introductionmentioning

confidence: 99%

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Model Checking Quantitative Hyperproperties

Finkbeiner

Hahn

Torfah

2018

Computer Aided Verification

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Abstract. Hyperproperties are properties of sets of computation traces. In this paper, we study quantitative hyperproperties, which we define as hyperproperties that express a bound on the number of traces that may appear in a certain relation. For example, quantitative non-interference limits the amount of information about certain secret inputs that is leaked through the observable outputs of a system. Quantitative noninterference thus bounds the number of traces that have the same observable input but different observable output. We study quantitative hyperproperties in the setting of HyperLTL, a temporal logic for hyperproperties. We show that, while quantitative hyperproperties can be expressed in HyperLTL, the running time of the HyperLTL model checking algorithm is, depending on the type of property, exponential or even doubly exponential in the quantitative bound. We improve this complexity with a new model checking algorithm based on model-counting. The new algorithm needs only logarithmic space in the bound and therefore improves, depending on the property, exponentially or even doubly exponentially over the model checking algorithm of HyperLTL. In the worst case, the new algorithm needs polynomial space in the size of the system. Our Max#Sat-based prototype implementation demonstrates, however, that the counting approach is viable on systems with nontrivial quantitative information flow requirements such as a passcode checker.

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“…We compare the Max#Sat-based approach to the expansionbased approach using HyperLTL [26]. Our implementation uses the MaxCount tool [29].…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Example 3 (Quantitative Non-interference In Hyperltl)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model Checking Quantitative Hyperproperties

Finkbeiner

Hahn

Torfah

2018

Computer Aided Verification

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“…None of the tools produced any wrong solutions. 17 Parallel Mode. Table 5 summarizes the experimental results, again including the number of solved benchmarks, the uniquely solved instances, the number of solutions that could not be verified within the timeout, and the accumulated quality of solutions.…”

Section: Aiger/safety-track: Synthesismentioning

confidence: 99%

The 4th Reactive Synthesis Competition (SYNTCOMP 2017): Benchmarks, Participants & Results

Jacobs

Basset

Bloem

et al. 2017

Electron. Proc. Theor. Comput. Sci.

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We report on the fourth reactive synthesis competition (SYNTCOMP 2017). We introduce two new benchmark classes that have been added to the SYNTCOMP library, and briefly describe the benchmark selection, evaluation scheme and the experimental setup of SYNTCOMP 2017. We present the participants of SYNTCOMP 2017, with a focus on changes with respect to the previous years and on the two completely new tools that have entered the competition. Finally, we present and analyze the results of our experimental evaluation, including a ranking of tools with respect to quantity and quality of solutions.

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MGHyper: Checking Satisfiability of HyperLTL Formulas Beyond the $$\exists ^\forall ^$$∃∗∀∗ Fragment

Finkbeiner

Hahn

Hans

2018

Automated Technology for Verification and Analysis

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Hyperproperties are properties that refer to multiple computation traces. This includes many information-flow security policies, such as observational determinism, (generalized) noninterference, and noninference, and other system properties like symmetry or Hamming distances between in error-resistant codes. We introduce MGHyper, a tool for automatic satisfiability checking and model generation for hyperproperties expressed in HyperLTL. Unlike previous satisfiability checkers, MGHyper is not limited to the decidable ∃ * ∀ * fragment of Hy-perLTL, but provides a semi-decisionprocedure for the full logic. An important application of MGHyper is to automatically check equivalences between different hyperproperties (and different formalizations of the same hyperproperty) and to build counterexamples that disprove a certain claimed implication. We describe the semi-decisionprocedure implemented in MGHyper and report on experimental results obtained both with typical hyperproperties from the literature and with randomly generated HyperLTL formulas.

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Algorithms for Model Checking HyperLTL and HyperCTL $$^*$$

Cited by 159 publications

References 33 publications

Model Checking Quantitative Hyperproperties

Model Checking Quantitative Hyperproperties

The 4th Reactive Synthesis Competition (SYNTCOMP 2017): Benchmarks, Participants & Results

MGHyper: Checking Satisfiability of HyperLTL Formulas Beyond the $$\exists ^\forall ^$$∃∗∀∗ Fragment

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Algorithms for Model Checking HyperLTL and HyperCTL $$^*$$

Cited by 159 publications

References 33 publications

Model Checking Quantitative Hyperproperties

Model Checking Quantitative Hyperproperties

The 4th Reactive Synthesis Competition (SYNTCOMP 2017): Benchmarks, Participants & Results

MGHyper: Checking Satisfiability of HyperLTL Formulas Beyond the $$\exists ^*\forall ^*$$∃∗∀∗ Fragment

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MGHyper: Checking Satisfiability of HyperLTL Formulas Beyond the $$\exists ^\forall ^$$∃∗∀∗ Fragment