Untraceable electronic mail, return addresses, and digital pseudonyms
Communications of the ACM
Timing Attacks on Implementations of Diffie-Hellman, RSA, DSS, and Other Systems
CRYPTO '96 Proceedings of the 16th Annual International Cryptology Conference on Advances in Cryptology
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
An information-theoretic model for adaptive side-channel attacks
Proceedings of the 14th ACM conference on Computer and communications security
Anonymity protocols as noisy channels
Information and Computation
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
The Complexity of Distinguishing Distributions (Invited Talk)
ICITS '08 Proceedings of the 3rd international conference on Information Theoretic Security
On the Bayes risk in information-hiding protocols
Journal of Computer Security - 20th IEEE Computer Security Foundations Symposium (CSF)
Formally Bounding the Side-Channel Leakage in Unknown-Message Attacks
ESORICS '08 Proceedings of the 13th European Symposium on Research in Computer Security: Computer Security
On the Foundations of Quantitative Information Flow
FOSSACS '09 Proceedings of the 12th International Conference on Foundations of Software Science and Computational Structures: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009
Quantifying information leakage in process calculi
Information and Computation
Quantitative Notions of Leakage for One-try Attacks
Electronic Notes in Theoretical Computer Science (ENTCS)
A Provably Secure and Efficient Countermeasure against Timing Attacks
CSF '09 Proceedings of the 2009 22nd IEEE Computer Security Foundations Symposium
The bayesian traffic analysis of mix networks
Proceedings of the 16th ACM conference on Computer and communications security
Compositional methods for information-hiding
FOSSACS'08/ETAPS'08 Proceedings of the Theory and practice of software, 11th international conference on Foundations of software science and computational structures
Vulnerability Bounds and Leakage Resilience of Blinded Cryptography under Timing Attacks
CSF '10 Proceedings of the 2010 23rd IEEE Computer Security Foundations Symposium
QEST '10 Proceedings of the 2010 Seventh International Conference on the Quantitative Evaluation of Systems
Asymptotic information leakage under one-try attacks
FOSSACS'11/ETAPS'11 Proceedings of the 14th international conference on Foundations of software science and computational structures: part of the joint European conferences on theory and practice of software
On the asymptotics of M-hypothesis Bayesian detection
IEEE Transactions on Information Theory
Provable de-anonymization of large datasets with sparse dimensions
POST'12 Proceedings of the First international conference on Principles of Security and Trust
Worst- and average-case privacy breaches in randomization mechanisms
TCS'12 Proceedings of the 7th IFIP TC 1/WG 202 international conference on Theoretical Computer Science
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We 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?8, 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.