Quantitative Information Flow, Relations and Polymorphic Types

Clark, David

doi:10.1093/logcom/exi009

Cited by 108 publications

(135 citation statements)

References 36 publications

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“…Clark, Hunt and Malacaria [21,22] have done some interesting work using information theoretic ideas as a basis for studying information flow. This general theme of incorporating quantitative and probabilistic techniques into program analysis and reasoning is certain to expand.…”

Section: A Probabilistic Version Of Agat's Transformationmentioning

confidence: 99%

Program Analysis Probably Counts

Pierro¹,

Hankin²,

Wiklicky³

2009

The Computer Journal

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Abstract. Semantics-based program analysis uses an abstract semantics of programs/systems to statically determine run-time properties. Classic examples from compiler technology include analyses to support constant propagation and constant folding transformations and estimation of pointer values to prevent buffer overruns. More recent examples include the estimation of information flows (to enforce security constraints) and estimation of non-functional properties such as timing (to determine worst case execution times in hard real-time applications). The classical approaches are based on semantics involving discrete mathematics. Paralleling trends in model-checking, there have been recent moves towards using probabilistic and quantitative methods in program analysis. In this paper we will start by reviewing both classical and probabilistic/quantitative approaches to program analysis. We will provide a comparison of the two approaches. We will use a simple information flow analysis to exemplify the classical approach. The existence of covert information flows through timing channels are difficult to detect using classical techniques; we show how such problems can be addressed using probabilistic techniques.

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Section: A Probabilistic Version Of Agat's Transformationmentioning

confidence: 99%

Program Analysis Probably Counts

Pierro¹,

Hankin²,

Wiklicky³

2009

The Computer Journal

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“…Consequently, there have been various attempts to define the degree of protection by using concepts based on Shannon entropy, notably mutual information [13,14,15,16] and the related notion of capacity, which is the supremum of the mutual information over all possible input distributions, and which therefore represents the worst case from the point of view of security [17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Quantifying leakage in the presence of unreliable sources of information

Hamadou

Palamidessi

Sassone

2017

Journal of Computer and System Sciences

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Belief and min-entropy leakage are two well-known approaches to quantify information flow in security systems. Both concepts stand as alternatives to the traditional approaches founded on Shannon entropy and mutual information, which were shown to provide inadequate security guarantees. In this paper we unify the two concepts in one model so as to cope with the frequent (potentially inaccurate, misleading or outdated) attackers' side information about individuals on social networks, online forums, blogs and other forms of online communication and information sharing. To this end we propose a new metric based on min-entropy that takes into account the adversary's beliefs.

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confidence: 99%

“…Several works in literature use an Information Theoretic approach to model the problem and define the leakage in a quantitative way, see for example [17,4,9,10,13,12,2]. The idea is that the system is seen as a channel.…”

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confidence: 99%

Entropy and Attack Models in Information Flow

Alvim

Andrés

Palamidessi

2010

IFIP Advances in Information and Communication Technology

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In recent years, there has been a growing interest in considering the quantitative aspects of Information Flow, partly because often the a priori knowledge of the secret information can be represented by a probability distribution, and partly because the mechanisms to protect the information may use randomization to obfuscate the relation between the secrets and the observables.Several works in literature use an Information Theoretic approach to model the problem and define the leakage in a quantitative way, see for example [17,4,9,10,13,12,2]. The idea is that the system is seen as a channel. The input represents the secret, the output represents the observable, and the correlation between the input and output (mutual information) represents the information leakage. The worst case leakage corresponds then to the capacity of the channel, which is by definition the maximum mutual information that can be obtained by varying the input distribution.In the works mentioned above, the notion of mutual information is based on Shannon entropy, which (because of its mathematical properties) is the most established measure of uncertainty. From the security point of view, this measure corresponds to a particular model of attack and a particular way of estimating the security threat (vulnerability of the secret). Other notions have been considered, and argued to be more appropriate for security in certain scenarios. These include: Rényi min-entropy [1,16], Bayes risk [3], guessing entropy [11], and marginal guesswork [14]. Köpf and Basin discuss the relation between bruteforce guessing attacks and entropy in [8], in the context of information flow induced by a deterministic program, and define the information leakage as difference between the input entropy and the conditional one, namely the entropy based on the a priori input distribution, and the entropy of the a posteriori distribution (i.e. after observing teh output), respectively. One of their main results is that, in their framework, the notion of leakage under the various notions of attacks considered in their paper is always non-negative.In this talk, we extend the analysis of Köpf and Basin to the probabilistic scenario, and we consider also other notions of entropy, including the family of entropies proposed by Rényi [15]. We argue that in the probabilistic case the notion of information leakage needs to be revised. In fact, when the same secret can give different observables (according to a probability distribution),

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Quantitative Information Flow, Relations and Polymorphic Types

Cited by 108 publications

References 36 publications

Program Analysis Probably Counts

Program Analysis Probably Counts

Quantifying leakage in the presence of unreliable sources of information

Entropy and Attack Models in Information Flow

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