TY  - CHAP
Y1  - 2011///
UR  - http://dx.doi.org/10.1007/978-3-642-19805-2_27
M1  - 6604
A1  - Boreale, Michele
A1  - Pampaloni, Francesca
A1  - Paolini, Michela
KW  - Security; quantitative information leakage; information theory; Bayes risk; hidden Markov models 
AV  - public
N1  - Proceedings of the 14th International Conference, FOSSACS 2011, Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2011, Saarbrücken, Germany, March 26?April 3, 2011
T3  - Lecture Notes in Computer Science
TI  - Asymptotic information leakage under one-try attacks
SP  - 396
PB  - Springer
T2  - Foundations of Software Science and Computational Structures
SN  - 978-3-642-19804-5
ED  - Hoffman, Martin
N2  - We 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.
EP  - 410
ID  - eprints1243
ER  -