IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-11-10T22:25:41ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2012-03-27T09:53:56Z2014-07-28T12:21:38Zhttp://eprints.imtlucca.it/id/eprint/1244This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/12442012-03-27T09:53:56ZQuantitative information flow, with a viewWe put forward a general model intended for assessment of system security against passive eavesdroppers, both quantitatively ( how much information is leaked) and qualitatively ( what properties are leaked). To this purpose, we extend information hiding systems ( ihs ), a model where the secret-observable relation is represented as a noisy channel, with views : basically, partitions of the state-space. Given a view W and n independent observations of the system, one is interested in the probability that a Bayesian adversary wrongly predicts the class of W the underlying secret belongs to. We offer results that allow one to easily characterise the behaviour of this error probability as a function of the number of observations, in terms of the channel matrices defining the ihs and the view W . In particular, we provide expressions for the limit value as n → ∞, show by tight bounds that convergence is exponential, and also characterise the rate of convergence to predefined error thresholds. We then show a few instances of statistical attacks that can be assessed by a direct application of our model: attacks against modular exponentiation that exploit timing leaks, against anonymity in mix-nets and against privacy in sparse datasets.Michele BorealeFrancesca Pampalonifrancesca.pampaloni@imtlucca.itMichela Paolinimichela.paolini@alumni.imtlucca.it2012-03-27T09:38:21Z2014-07-28T12:21:19Zhttp://eprints.imtlucca.it/id/eprint/1243This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/12432012-03-27T09:38:21ZAsymptotic information leakage under one-try attacksWe study the asymptotic behaviour of (a) information leakage and (b) adversary’s error probability in information hiding systems modelled as noisy channels. Specifically, we assume the attacker can make a single guess after observing n independent executions of the system, throughout which the secret information is kept fixed. We show that the asymptotic behaviour of quantities (a) and (b) can be determined in a simple way from the channel matrix. Moreover, simple and tight bounds on them as functions of n show that the convergence is exponential. We also discuss feasible methods to evaluate the rate of convergence. Our results cover both the Bayesian case, where a prior probability distribution on the secrets is assumed known to the attacker, and the maximum-likelihood case, where the attacker does not know such distribution. In the Bayesian case, we identify the distributions that maximize the leakage. We consider both the min-entropy setting studied by Smith and the additive form recently proposed by Braun et al., and show the two forms do agree asymptotically. Next, we extend these results to a more sophisticated eavesdropping scenario, where the attacker can perform a (noisy) observation at each state of the computation and the systems are modelled as hidden Markov models.Michele BorealeFrancesca Pampalonifrancesca.pampaloni@imtlucca.itMichela Paolinimichela.paolini@alumni.imtlucca.it