Title
Optimal Error Correction for Computationally Bounded Noise
Abstract
For adversarial but computationally bounded models of error, we construct appealingly simple and efficient cryptographic encoding and unique decoding schemes whose error-correction capability is much greater than classically possible. In particular: 1) For binary alphabets, we construct positive-rate coding schemes that are uniquely decodable under a 1/2 - γ error rate for any constant γ > 0. 2) For large alphabets, we construct coding schemes that are uniquely decodable under a 1 - R error rate for any information rate R > 0. Our results for large alphabets are actually optimal, since the "computationally bounded but adversarial channel" can simulate the behavior of the q-ary symmetric channel, where q denotes the size of the alphabet, the capacity of which is known to be upper-bounded by 1 - R. Our results hold under minimal assumptions on the communication infrastructure, namely: 1) we allow the channel to be more powerful than the receiver and 2) we only assume that some information about the sender-a public key-is known. (In particular, we do not require any shared secret key or joint local state between sender and receivers).
Year
DOI
Venue
2010
10.1109/TIT.2010.2070370
IEEE Transactions on Information Theory
Keywords
Field
DocType
binary codes,decoding,error correction,public key cryptography,binary alphabets,communication infrastructure,computationally bounded noise,construct coding schemes,cryptographic encoding,error-correction capability,optimal error correction,positive-rate coding schemes,q-ary symmetric channel,sender-a public key,unique decoding schemes,Adversarial error,channel modelling,computationally bounded channels
Discrete mathematics,Code rate,Computer science,Upper and lower bounds,Binary code,Word error rate,Communication channel,Algorithm,Error detection and correction,Decoding methods,Bounded function
Journal
Volume
Issue
ISSN
56
11
0018-9448
Citations 
PageRank 
References 
5
0.45
16
Authors
4
Name
Order
Citations
PageRank
Silvio Micali1114342581.31
Chris Peikert23840154.98
Madhu Sudan35616591.68
David A. Wilson4615.01