Approximation and Randomization for Quantitative Information-Flow Analysis

Köpf, Boris; Rybalchenko, Andrey

doi:10.1109/csf.2010.8

Cited by 57 publications

(71 citation statements)

References 46 publications

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“…A quite different approach to approximating leakage is given in the recent work of Köpf and Rybalchenko [34], which uses statistical sampling to estimate the mutualinformation leakage of a deterministic imperative program from input to output , under a uniform a priori distribution. While they present the technique in terms of estimating ( | ), it is clearer to remember that the mutualinformation leakage is just ( ).…”

Section: Techniques For Computing Min-entropy Leakagementioning

confidence: 99%

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Quantifying Information Flow Using Min-Entropy

Smith

2011

2011 Eighth International Conference on Quantitative Evaluation of SysTems

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Abstract-Quantitative theories of information flow are of growing interest, due to the fundamental importance of protecting confidential information from improper disclosure, together with the unavoidability of "small" leaks in practical systems. But while it is tempting to measure leakage using classic information-theoretic concepts like Shannon entropy and mutual information, these turn out not to provide very satisfactory security guarantees. As a result, several researchers have developed an alternative theory based on Rényi's minentropy. In this theory, uncertainty is measured in terms of a random variable's vulnerability to being guessed in one try by an adversary; note that this is the complement of the Bayes Risk. In this paper, we survey the main theory of min-entropy leakage in deterministic and probabilistic systems, including comparisons with mutual information leakage, results on mincapacity, results on channels in cascade, and techniques for calculating min-entropy leakage in systems.

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Section: Techniques For Computing Min-entropy Leakagementioning

confidence: 99%

“…Nevertheless, this is an area that is now seeing a great deal of work, both in the context of probabilistic systems [32], [33] and deterministic imperative programs [8], [34], [35], [36].…”

Section: Techniques For Computing Min-entropy Leakagementioning

confidence: 99%

Quantifying Information Flow Using Min-Entropy

Smith

2011

2011 Eighth International Conference on Quantitative Evaluation of SysTems

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“…We also leverage techniques from quantitative-information-flow analysis that enable establishing bounds for the amount of information that a program leaks about its input. One key observation is that (an upper bound on) the number of reachable states of a program corresponds to (an upper bound on) the number of leaked bits [31,42]. Such upper bounds can be obtained by computing super-sets of the set of reachable states by abstract interpretation, and by determining their sizes [31].…”

Section: Introductionmentioning

confidence: 99%

Automatic Quantification of Cache Side-Channels

Köpf

Mauborgne

Ochoa

2012

Computer Aided Verification

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Abstract. The latency gap between caches and main memory has been successfully exploited for recovering sensitive input to programs, such as cryptographic keys from implementation of AES and RSA. So far, there are no practical general-purpose countermeasures against this threat. In this paper we propose a novel method for automatically deriving upper bounds on the amount of information about the input that an adversary can extract from a program by observing the CPU's cache behavior. At the heart of our approach is a novel technique for efficient counting of concretizations of abstract cache states that enables us to connect state-of-the-art techniques for static cache analysis and quantitative information-flow. We implement our counting procedure on top of the AbsInt TimingExplorer, one of the most advanced engines for static cache analysis. We use our tool to perform a case study where we derive upper bounds on the cache leakage of a 128-bit AES executable on an ARM processor with a realistic cache configuration. We also analyze this implementation with a commonly suggested (but until now heuristic) countermeasure applied, obtaining a formal account of the corresponding increase in security.

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“…Statistical sampling is used by Chatzikokolakis et al (2010) and Köpf and Rybalchenko (2010). Also, the computational complexity of quantitative information flow problems is studied by Yasuoka and Terauchi (2010).…”

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confidence: 99%

Preface to the special issue on quantitative information flow

Andrés¹,

Palamidessi²,

Smith³

2014

Math. Struct. Comp. Sci.

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A long-standing and fundamental issue in computer security is to control the flow of information, whether to prevent confidential information from being leaked, or to prevent trusted information from being tainted. While there have been many efforts aimed at preventing improper flows completely (see for example, the survey by Sabelfeld and Myers (2003)), it has long been recognized that perfection is often impossible in practice. A basic example is a login program -whenever it rejects an incorrect password, it unavoidably reveals that the secret password differs from the one that was entered. More subtly, systems may be vulnerable to side channel attacks, because observable characteristics like running time and power consumption may depend, at least partially, on sensitive information.For these reasons, the possibility of quantifying information flow becomes attractive, as this could allow certain improper flows to be tolerated on the grounds that they are 'small'. While there was early work on quantitative information flow by Denning (1983), Millen (1987, McLean (1990) and Gray (1991), the area received relatively little attention until the past decade, when it was revitalized starting with the efforts of Clark, Hunt, and Malacaria (2001).In the past decade, there has been too much work for a comprehensive survey here, but we can briefly describe the main themes that have been explored.From the perspective of foundations, there have been a variety of studies aimed at defining quantitative measures of information flow for a variety of system models, establishing the operational significance of the measures with respect to security, and establishing their mathematical properties, including relationships among the different measures that have been considered. Papers with a foundational focus include those of

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Approximation and Randomization for Quantitative Information-Flow Analysis

Cited by 57 publications

References 46 publications

Quantifying Information Flow Using Min-Entropy

Quantifying Information Flow Using Min-Entropy

Automatic Quantification of Cache Side-Channels

Preface to the special issue on quantitative information flow

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