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6 Randomness Extractors
"... Randomness extractors are functions that extract almostuniform bits from sources of biased and correlated bits. The original motivation for extractors was to simulate randomized algorithms with weak random sources as might arise in nature. This motivation is still compelling, but extractors have ta ..."
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Randomness extractors are functions that extract almostuniform bits from sources of biased and correlated bits. The original motivation for extractors was to simulate randomized algorithms with weak random sources as might arise in nature. This motivation is still compelling, but extractors have
6 Randomness Extractors
"... Randomness extractors are functions that extract almostuniform bits from sources of biased and correlated bits. The original motivation for extractors was to simulate randomized algorithms with weak random sources as might arise in nature. This motivation is still compelling, but extractors have ta ..."
Abstract
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Randomness extractors are functions that extract almostuniform bits from sources of biased and correlated bits. The original motivation for extractors was to simulate randomized algorithms with weak random sources as might arise in nature. This motivation is still compelling, but extractors have
6 Randomness Extractors
"... Randomness extractors are functions that extract almostuniform bits from sources of biased and correlated bits. The original motivation for extractors was to simulate randomized algorithms with weak random sources as might arise in nature. This motivation is still compelling, but extractors have ta ..."
Abstract
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Randomness extractors are functions that extract almostuniform bits from sources of biased and correlated bits. The original motivation for extractors was to simulate randomized algorithms with weak random sources as might arise in nature. This motivation is still compelling, but extractors have
Randomness extractors  applications and constructions
 LIPICS LEIBNIZ INTERNATIONAL PROCEEDINGS IN INFORMATICS
, 2009
"... Randomness extractors are efficient algorithms which convert weak random sources into nearly perfect ones. While such purification of randomness was the original motivation for constructing extractors, these constructions turn out to have strong pseudorandom properties which found applications in ..."
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Randomness extractors are efficient algorithms which convert weak random sources into nearly perfect ones. While such purification of randomness was the original motivation for constructing extractors, these constructions turn out to have strong pseudorandom properties which found applications
Randomness Extractors and their Many Guises
"... Since its introduction by Nisan and Zuckerman (STOC ‘93) nearly a decade ago, the notion of a randomness extractor has proven to be a fundamental and powerful one. Extractors and their variants have found widespread application in a variety of areas, including pseudorandomness and derandomization, c ..."
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Since its introduction by Nisan and Zuckerman (STOC ‘93) nearly a decade ago, the notion of a randomness extractor has proven to be a fundamental and powerful one. Extractors and their variants have found widespread application in a variety of areas, including pseudorandomness and derandomization
An introduction to randomness extractors
"... We give an introduction to the area of “randomness extraction” and survey the main concepts of this area: deterministic extractors, seeded extractors and multiple sources extractors. For each one we briefly discuss background, definitions, explicit constructions and applications. ..."
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Cited by 13 (2 self)
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We give an introduction to the area of “randomness extraction” and survey the main concepts of this area: deterministic extractors, seeded extractors and multiple sources extractors. For each one we briefly discuss background, definitions, explicit constructions and applications.
S.: HMAC is a randomness extractor and applications to TLS
, 2008
"... Abstract. In this paper, we study the security of a practical randomness extractor and its application in the tls standard. Randomness extraction is the first stage of key derivation functions since the secret shared between the entities does not always come from a uniformly distributed source. More ..."
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Cited by 13 (1 self)
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Abstract. In this paper, we study the security of a practical randomness extractor and its application in the tls standard. Randomness extraction is the first stage of key derivation functions since the secret shared between the entities does not always come from a uniformly distributed source
HMAC is a Randomness Extractor and Applications to TLS ABSTRACT
"... In this paper, we study the security of a practical randomness extractor and its application in the tls standard. Randomness extraction is the first stage of key derivation functions since the secret shared between the entities does not always come from a uniformly distributed source. More precisely ..."
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In this paper, we study the security of a practical randomness extractor and its application in the tls standard. Randomness extraction is the first stage of key derivation functions since the secret shared between the entities does not always come from a uniformly distributed source. More
Twosources Randomness Extractors for Elliptic Curves
"... Abstract. This paper studies the task of twosources randomness extractors for elliptic curves dened over nite elds K, where K can be a prime or a binary eld. In fact, we introduce new constructions of functions over elliptic curves which take in input two random points from two differents subgrou ..."
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Abstract. This paper studies the task of twosources randomness extractors for elliptic curves dened over nite elds K, where K can be a prime or a binary eld. In fact, we introduce new constructions of functions over elliptic curves which take in input two random points from two differents
How to get more mileage from randomness extractors
, 2007
"... Let C be a class of distributions over {0, 1}^n. A deterministic randomness extractor for C isa function E: {0, 1}n! {0, 1}m such that for any X in C the distribution E(X) is statisticallyclose to the uniform distribution. A long line of research deals with explicit constructions of such extractors ..."
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Cited by 17 (5 self)
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Let C be a class of distributions over {0, 1}^n. A deterministic randomness extractor for C isa function E: {0, 1}n! {0, 1}m such that for any X in C the distribution E(X) is statisticallyclose to the uniform distribution. A long line of research deals with explicit constructions of such extractors
Results 1  10
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