Guessing and entropy

Massey, James L.

doi:10.1109/isit.1994.394764

Cited by 385 publications

(368 citation statements)

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“…It measures the average number of key candidates to test after the side-channel attack. The guessing entropy was originally defined in [26] and has been proposed to quantify the effectiveness of adaptive side-channel attacks in [23]. It can be related to the notion of gain that has been used in the context of multiple linear cryptanalysis to measure how much the complexity of an exhaustive key search is reduced thanks to an attack [5].…”

Section: Actual Security Metricsmentioning

confidence: 99%

A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks

Standaert

Malkin

Yung

2009

Lecture Notes in Computer Science

687

557

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Abstract. The fair evaluation and comparison of side-channel attacks and countermeasures has been a long standing open question, limiting further developments in the field. Motivated by this challenge, this work makes a step in this direction and proposes a framework for the analysis of cryptographic implementations that includes a theoretical model and an application methodology. The model is based on commonly accepted hypotheses about side-channels that computations give rise to. It allows quantifying the effect of practically relevant leakage functions with a combination of information theoretic and security metrics, measuring the quality of an implementation and the strength of an adversary, respectively. From a theoretical point of view, we demonstrate formal connections between these metrics and discuss their intuitive meaning. From a practical point of view, the model implies a unified methodology for the analysis of side-channel key recovery attacks. The proposed solution allows getting rid of most of the subjective parameters that were limiting previous specialized and often ad hoc approaches in the evaluation of physically observable devices. It typically determines the extent to which basic (but practically essential) questions such as "How to compare two implementations? " or "How to compare two side-channel adversaries? " can be answered in a sound fashion.

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Section: Actual Security Metricsmentioning

confidence: 99%

A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks

Standaert

Malkin

Yung

2009

Lecture Notes in Computer Science

687

557

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“…In particular we are interested in the following notions: entropy, mutual information, relative entropy and min-entropy. We refer the reader to [32,12] for more details.…”

Section: Some Notions Of Information Theorymentioning

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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“…First defined by Massey [15], the guessing entropy captures the expected number of guesses (with an optimum strategy) to correctly guess the value of a random variable (in our scenario the secret key). This can be linked to the key rank by observing that the key rank is the number of guesses an optimal adversary would take to guess the secret key.…”

Section: Theoretical Characterization Of the Key Rank Distributionmentioning

confidence: 99%

Characterisation and Estimation of the Key Rank Distribution in the Context of Side Channel Evaluations

Martin

Mather²,

Oswald

et al. 2016

Lecture Notes in Computer Science

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Abstract. Quantifying the side channel security of implementations has been a significant research question for several years in academia but also among real world side channel practitioners. As part of security evaluations, efficient key rank estimation algorithms were devised, which in contrast to analyses based on subkey recovery, give a holistic picture of the security level after a side channel attack. However, it has been observed that outcomes of rank estimations show a huge spread in precisely the range of key ranks where enumeration could lead to key recovery. These observations raise the question whether this is because of insufficient rank estimation procedures, or, if this is an inherent property of the key rank. Furthermore, if this was inherent, how could key rank outcomes be translated into practically meaningful figures, suitable to analysing the risk that real world side channel attacks pose? This paper is a direct response to these questions. We experimentally identify the key rank distribution and show that it is independent of different distinguishers and signal-to-noise ratios. Then we offer a theoretical explanation for the observed key rank distribution and determine how many samples thereof are required for a robust estimation of some key parameters. We discuss how this can be naturally integrated into real world side channel evaluation practices. We conclude our research by connecting non-parametric order statistics, in particular percentiles, in a practically meaningful way with business goals.

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Guessing and entropy

Cited by 385 publications

References 1 publication

A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks

A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks

Quantifying leakage in the presence of unreliable sources of information

Characterisation and Estimation of the Key Rank Distribution in the Context of Side Channel Evaluations

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