Communications of the ACM
Authentication theory/coding theory
Proceedings of CRYPTO 84 on Advances in cryptology
A note on computational indistinguishability
Information Processing Letters
On the Computational Complexity of Approximating Distributions by Probabilistic Automata
Machine Learning - Computational learning theory
Inference and minimization of hidden Markov chains
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Efficient distribution-free learning of probabilistic concepts
Journal of Computer and System Sciences - Special issue: 31st IEEE conference on foundations of computer science, Oct. 22–24, 1990
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
On the learnability of discrete distributions
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
An introduction to computational learning theory
An introduction to computational learning theory
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
A Pseudorandom Generator from any One-way Function
SIAM Journal on Computing
Foundations of Cryptography: Basic Tools
Foundations of Cryptography: Basic Tools
A personal view of average-case complexity
SCT '95 Proceedings of the 10th Annual Structure in Complexity Theory Conference (SCT'95)
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
The Complexity of Online Memory Checking
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
ICML '06 Proceedings of the 23rd international conference on Machine learning
Authentication theory and hypothesis testing
IEEE Transactions on Information Theory
ICML '06 Proceedings of the 23rd international conference on Machine learning
The complexity of online memory checking
Journal of the ACM (JACM)
Protocols and lower bounds for failure localization in the internet
EUROCRYPT'08 Proceedings of the theory and applications of cryptographic techniques 27th annual international conference on Advances in cryptology
| Hi-index | 0.00 |
Consider Alice and Bob, who have some shared secret which helps Alice to identify Bob-impersonators, and Eve, who does not know their secret. Eve wants to impersonate Bob and "fool" Alice. If Eve is computationally unbounded, how long does she need to observe Bob before she can impersonate him? What is a good strategy for Eve? If (cryptographic) one-way functions exist, an efficient Eve cannot impersonate even very simple Bobs, but if they do not exist, can Eve learn to impersonate any efficient Bob?We formalize these questions in a new computational learning model, which we believe captures a wide variety of natural learning tasks, and tightly bound the number of observations Eve makes in terms of the secret's entropy. We then show that if one-way functions do not exist, then an efficient Eve can learn to impersonate any efficient Bob nearly as well as an unbounded Eve.For the full version of this work see (Naor & Rothblum, 2006).