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Secure Information Flow by SelfComposition
 PROCEEDINGS OF CSFW’04
, 2004
"... Noninterference is a highlevel security property that guarantees the absence of illicit information leakages through a program execution. A common means to enforce noninterference is to use an information flow type system. However, such type systems are inherently imprecise, and reject many secur ..."
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Cited by 80 (8 self)
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Noninterference is a highlevel security property that guarantees the absence of illicit information leakages through a program execution. A common means to enforce noninterference is to use an information flow type system. However, such type systems are inherently imprecise, and reject many secure programs, even for simple programming languages. The purpose of this paper is to propose a logical formulation of noninterference that allows a more precise analysis or programs, and that is amenable to deductive verification techniques, such as programming logics and weakest precondition calculi, and algorithmic verification techniques such as modelchecking. We illustrate the applicability of our method in several scenarii, including a simple imperative language, a nondeterministic language, and finally a language with shared mutable data structures.
Reasoning about probabilistic sequential programs ∗
"... A complete and decidable Hoarestyle calculus for iterationfree probabilistic sequential programs is presented using a state logic with truthfunctional propositional (not arithmetical) connectives. 1 ..."
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Cited by 12 (11 self)
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A complete and decidable Hoarestyle calculus for iterationfree probabilistic sequential programs is presented using a state logic with truthfunctional propositional (not arithmetical) connectives. 1
PROBMELA: a modeling language for communicating probabilistic processes
, 2004
"... Building automated tools to address the analysis of reactive probabilistic systems requires a simple, but expressive input language with a formal semantics based on a probabilistic operational model that can serve as starting point for verification algorithms. We introduce a higher level description ..."
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Cited by 10 (3 self)
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Building automated tools to address the analysis of reactive probabilistic systems requires a simple, but expressive input language with a formal semantics based on a probabilistic operational model that can serve as starting point for verification algorithms. We introduce a higher level description language for probabilistic parallel programs with shared variables, message passing via synchronous and (perfect or lossy) fifo channels and atomic regions and provide a structured operational semantics. Applied to finitestate systems, the semantics can serve as basis for the algorithmic generation of a Markov decision process that models the stepwise behavior of the given system.
A probabilistic hoarestyle logic for gamebased cryptographic proofs (long version
 In ICALP’06, volume 4052 of LNCS
, 2005
"... Abstract. We extend a Probabilistic Hoarestyle logic to formalize gamebased cryptographic proofs. Our approach provides a systematic and rigorous framework, thus preventing errors from being introduced. We illustrate our technique by proving semantic security of ElGamal. 1 ..."
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Cited by 7 (0 self)
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Abstract. We extend a Probabilistic Hoarestyle logic to formalize gamebased cryptographic proofs. Our approach provides a systematic and rigorous framework, thus preventing errors from being introduced. We illustrate our technique by proving semantic security of ElGamal. 1
Exogenous Probabilistic Computation Tree Logic
"... Replace this file with prentcsmacro.sty for your meeting, or with entcsmacro.sty for your meeting. Both can be ..."
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Cited by 4 (1 self)
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Replace this file with prentcsmacro.sty for your meeting, or with entcsmacro.sty for your meeting. Both can be
A systematic approach to probabilistic pointer analysis
 In ASPLAS ’07
"... Abstract. We present a formal framework for syntax directed probabilistic program analysis. Our focus is on probabilistic pointer analysis. We show how to obtain probabilistic pointsto matrices and their relational counterparts in a systematic way via Probabilistic Abstract Interpretation (PAI). Th ..."
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Cited by 3 (1 self)
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Abstract. We present a formal framework for syntax directed probabilistic program analysis. Our focus is on probabilistic pointer analysis. We show how to obtain probabilistic pointsto matrices and their relational counterparts in a systematic way via Probabilistic Abstract Interpretation (PAI). The analysis is based on a nonstandard semantics for a simple imperative language which corresponds to a DiscreteTime Markov Chain (DTMC). The generator of this DTMC is constructed by composing (via tensor product) the probabilistic control flow of the program and the data updates of the different variables at individual program points. The dimensionality of the concrete semantics is in general prohibitively large but abstraction (via PAI) allows for a drastic (exponential) reduction of size. 1
of probabilistic programs with bounded resources
"... algebra techniques for deciding the correctness ..."
On Probabilistic Techniques for Data Flow Analysis
"... We present a semanticsbased technique for analysing probabilistic properties of imperative programs. This consists in a probabilistic version of classical data flow analysis. We apply this technique to pWhile programs, i.e programs written in a probabilistic version of a simple While language. As a ..."
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We present a semanticsbased technique for analysing probabilistic properties of imperative programs. This consists in a probabilistic version of classical data flow analysis. We apply this technique to pWhile programs, i.e programs written in a probabilistic version of a simple While language. As a first step we introduce a syntax based definition of a linear operator semantics (LOS) which is equivalent to the standard structural operational semantics of While. The LOS of a pWhile program can be seen as the generator of a Discrete Time Markov Chain and plays a similar role as a collecting or trace semantics for classical While. Probabilistic Abstract Interpretation techniques are then employed in order to define data flow analyses for properties like Parity and Live Variables.
Exogenous Logics for Reasoning about Probabilistic Systems
"... Abstract. We define exogenous logics for reasoning about probabilistic systems: a probabilistic state logic EPPL, and its fixpoint extension MEPL, which is enriched with operators from the modal µcalculus. System states correspond to probability distributions over classical states and the system ev ..."
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Abstract. We define exogenous logics for reasoning about probabilistic systems: a probabilistic state logic EPPL, and its fixpoint extension MEPL, which is enriched with operators from the modal µcalculus. System states correspond to probability distributions over classical states and the system evolution is modeled by parametrized Kripke structures that capture both stochastic and non–deterministic transitions. We introduce two approaches to the verification of properties expressed in these logics, one syntactic (a weakly complete Hilbert calculus) and the other semantic (a model–checking algorithm). The completeness proof of MEPL builds on the decidability of the existential theory of the real numbers and on a polynomialspace sat algorithm for EPPL. The model checking problem for MEPL is also analysed and the logic is related to previous work. The semantics of EPPL and MEPL are defined in terms of probability distributions over sets of propositional symbols, whereas the usual approaches are designed for reasoning about distributions over paths of possible behaviour. The intended application of our logics is as a specification formalism for properties of probabilistic systems. We illustrate the use of the logics for specifying system properties with some simple examples. 1.
Fixpoint Logics for Reasoning about Probabilistic Systems
, 2010
"... Abstract. We consider exogenous logics for reasoning about probabilistic systems: a variant of probabilistic state logic EPPL[24], and its fixpoint extension MEPL, which is enriched with operators from the modal µcalculus. System states correspond to probability distributions over classical states ..."
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Abstract. We consider exogenous logics for reasoning about probabilistic systems: a variant of probabilistic state logic EPPL[24], and its fixpoint extension MEPL, which is enriched with operators from the modal µcalculus. System states correspond to probability distributions over classical states and the system evolution is modeled by parametrized Kripke structures that capture both stochastic and non–deterministic transitions. We introduce two approaches to the verification of properties expressed in these logics, one syntactic (a weakly complete Hilbert calculus) and the other semantic (a model– checking algorithm). The completeness proof of MEPL builds on the decidability of the existential theory of the real numbers and on a polynomialspace sat algorithm for EPPL. The model checking problem for MEPL is also analysed and the logic is related to previous work. The semantics of EPPL and MEPL are defined in terms of probability distributions over sets of propositional symbols, whereas the usual approaches are designed for reasoning about distributions over paths of possible behaviour. The intended application of our logics is as a specification formalism for properties of probabilistic systems. We illustrate the use of the logics for specifying system properties with some simple examples. 1