Results 1  10
of
167
Generalized privacy amplification
 IEEE Transactions on Information Theory
, 1995
"... Abstract This paper provides a general treatment of privacy amplification by public discussion, a concept introduced by Bennett, Brassard, and Robert for a special scenario. Privacy amplification is a process that allows two parties to distill a secret key from a common random variable about which ..."
Abstract

Cited by 325 (19 self)
 Add to MetaCart
(Show Context)
Abstract This paper provides a general treatment of privacy amplification by public discussion, a concept introduced by Bennett, Brassard, and Robert for a special scenario. Privacy amplification is a process that allows two parties to distill a secret key from a common random variable about which an eavesdropper has partial information. The two parties generally know nothing about the eavesdropper’s information except that it satisfies a certain constraint. The results have applications to unconditionally secure secretkey agreement protocols and quantum cryptography, and they yield results on wiretap and broadcast channels for a considerably strengthened definition of secrecy capacity. Index Terms Cryptography, secretkey agreement, unconditional security, privacy amplification, wiretap channel, secrecy capacity, RCnyi entropy, universal hashing, quantum cryptography. I.
Simple Constructions of Almost kwise Independent Random Variables
, 1992
"... We present three alternative simple constructions of small probability spaces on n bits for which any k bits are almost independent. The number of bits used to specify a point in the sample space is (2 + o(1))(log log n + k/2 + log k + log 1 ɛ), where ɛ is the statistical difference between the dist ..."
Abstract

Cited by 303 (40 self)
 Add to MetaCart
We present three alternative simple constructions of small probability spaces on n bits for which any k bits are almost independent. The number of bits used to specify a point in the sample space is (2 + o(1))(log log n + k/2 + log k + log 1 ɛ), where ɛ is the statistical difference between the distribution induced on any k bit locations and the uniform distribution. This is asymptotically comparable to the construction recently presented by Naor and Naor (our size bound is better as long as ɛ < 1/(k log n)). An additional advantage of our constructions is their simplicity.
SmallBias Probability Spaces: Efficient Constructions and Applications
 SIAM J. Comput
, 1993
"... We show how to efficiently construct a small probability space on n binary random variables such that for every subset, its parity is either zero or one with "almost" equal probability. They are called fflbiased random variables. The number of random bits needed to generate the random var ..."
Abstract

Cited by 276 (13 self)
 Add to MetaCart
(Show Context)
We show how to efficiently construct a small probability space on n binary random variables such that for every subset, its parity is either zero or one with "almost" equal probability. They are called fflbiased random variables. The number of random bits needed to generate the random variables is O(log n + log 1 ffl ). Thus, if ffl is polynomially small, then the size of the sample space is also polynomial. Random variables that are fflbiased can be used to construct "almost" kwise independent random variables where ffl is a function of k. These probability spaces have various applications: 1. Derandomization of algorithms: many randomized algorithms that require only k wise independence of their random bits (where k is bounded by O(log n)), can be derandomized by using fflbiased random variables. 2. Reducing the number of random bits required by certain randomized algorithms, e.g., verification of matrix multiplication. 3. Exhaustive testing of combinatorial circui...
Our Data, Ourselves: Privacy via Distributed Noise Generation
 In EUROCRYPT
, 2006
"... Abstract. In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. The purpose of the noise generation is to create a distributed implementation of the privacypreserving statistical databases described in recent papers [14 ..."
Abstract

Cited by 152 (15 self)
 Add to MetaCart
(Show Context)
Abstract. In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. The purpose of the noise generation is to create a distributed implementation of the privacypreserving statistical databases described in recent papers [14,4,13]. In these databases, privacy is obtained by perturbing the true answer to a database query by the addition of a small amount of Gaussian or exponentially distributed random noise. The computational power of evenasimple form of these databases, when the queryis just of the form È i f(di), that is, the sum over all rows i in the database of a function f applied to the data in row i, has been demonstrated in [4]. A distributed implementation eliminates the need for a trusted database administrator. The results for noise generation are of independent interest. The generation of Gaussian noise introduces a technique for distributing shares of many unbiased coins with fewer executions of verifiable secret sharing than would be needed using previous approaches (reduced by afactorofn). The generation of exponentially distributed noise uses two shallow circuits: one for generating many arbitrarily but identically biased coins at an amortized cost of two unbiased random bits apiece, independent of the bias, and the other to combine bits of appropriate biases to obtain an exponential distribution. 1
ChernoffHoeffding bounds for applications with limited independence.
 In Proceedings of the 4th Annual ACMSIAM Symposium on Discrete Algorithms,
, 1993
"... ..."
Simulating BPP Using a General Weak Random Source
 ALGORITHMICA
, 1996
"... We show how to simulate BPP and approximation algorithms in polynomial time using the output from a ffisource. A ffisource is a weak random source that is asked only once for R bits, and must output an Rbit string according to some distribution that places probability no more than 2 \GammaffiR on ..."
Abstract

Cited by 124 (17 self)
 Add to MetaCart
We show how to simulate BPP and approximation algorithms in polynomial time using the output from a ffisource. A ffisource is a weak random source that is asked only once for R bits, and must output an Rbit string according to some distribution that places probability no more than 2 \GammaffiR on any particular string. We also give an application to the unapproximability of Max Clique.
On The Power Of TwoPoints Based Sampling
 Journal of Complexity
, 1989
"... The purpose of this note is to present a new sampling technique and to demonstrate some of its properties. The new technique consists of picking two elements at random, and deterministically generating (from them) a long sequence of pairwise independent elements. The sequence is guarantees to inters ..."
Abstract

Cited by 98 (17 self)
 Add to MetaCart
(Show Context)
The purpose of this note is to present a new sampling technique and to demonstrate some of its properties. The new technique consists of picking two elements at random, and deterministically generating (from them) a long sequence of pairwise independent elements. The sequence is guarantees to intersect, with high probability, any set of nonnegligible density. 1. Introduction In recent years the role of randomness in computation has become more and more dominant. Randomness was used to speed up sequential computations (e.g. primality testing, testing polynomial identities etc.), but its effect on parallel and distributed computation is even more impressive. In either cases the solutions are typically presented such that they are guarateed to produce the desired result with some nonnegligible probability. It is implicitly suggested that if a higher degree of confidence is required the algorithm should be run several times, each time using different coin tosses. Since the coin tosses f...
Extracting Randomness: A Survey and New Constructions
, 1999
"... this paper we do two things. First, we survey extractors and dispersers: what they are, how they can be designed, and some of their applications. The work described in the survey is due to a long list of research papers by various authors##most notably by David Zuckerman. Then, we present a new tool ..."
Abstract

Cited by 85 (3 self)
 Add to MetaCart
this paper we do two things. First, we survey extractors and dispersers: what they are, how they can be designed, and some of their applications. The work described in the survey is due to a long list of research papers by various authors##most notably by David Zuckerman. Then, we present a new tool for constructing explicit extractors and give two new constructions that greatly improve upon previous results. The new tool we devise, a merger," is a function that accepts d strings, one of which is uniformly distributed and outputs a single string that is guaranteed to be uniformly distributed. We show how to build good explicit mergers, and how mergers can be used to build better extractors. Using this, we present two new constructions. The first construction succeeds in extracting all of the randomness from any somewhat random source. This improves upon previous extractors that extract only some of the randomness from somewhat random sources with enough" randomness. The amount of truly random bits used by this extractor, however, is not optimal. The second extractor we build extracts only some of the randomness and works only for sources with enough randomness, but uses a nearoptimal amount of truly random bits. Extractors and dispersers have many applications in removing randomness" in various settings and in making randomized constructions explicit. We survey some of these applications and note whenever our new constructions yield better results, e.g., plugging our new extractors into a previous construction we achieve the first explicit Nsuperconcentrators of linear size and polyloglog(N) depth. ] 1999 Academic Press CONTENTS 1.
Computing with Very Weak Random Sources
, 1994
"... For any fixed 6> 0, we show how to simulate RP algorithms in time nO(‘Ogn) using the output of a 6source wath minentropy R‘. Such a weak random source is asked once for R bits; it outputs an Rbit string such that any string has probability at most 2Rc. If 6> 1 l/(k + l), our BPP simulatio ..."
Abstract

Cited by 73 (7 self)
 Add to MetaCart
For any fixed 6> 0, we show how to simulate RP algorithms in time nO(‘Ogn) using the output of a 6source wath minentropy R‘. Such a weak random source is asked once for R bits; it outputs an Rbit string such that any string has probability at most 2Rc. If 6> 1 l/(k + l), our BPP simulations take time no(‘og(k)n) (log(k) is the logarithm iterated k times). We also gave a polynomialtime BPP simulation using ChorGoldreich sources of minentropy Ro(’), which is optimal. We present applications to timespace tradeoffs, expander constructions, and the hardness of approximation. Also of interest is our randomnessefficient Leflover Hash Lemma, found independently by Goldreich & Wigderson.
Deterministic Extractors for BitFixing Sources and ExposureResilient Cryptography
 In Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
, 2003
"... Abstract. We give an efficient deterministic algorithm that extracts Ω(n2γ) almostrandom bits from sources where n 1 2 +γ of the n bits are uniformly random and the rest are fixed in advance. This improves upon previous constructions, which required that at least n/2 of the bits be random in order ..."
Abstract

Cited by 72 (2 self)
 Add to MetaCart
(Show Context)
Abstract. We give an efficient deterministic algorithm that extracts Ω(n2γ) almostrandom bits from sources where n 1 2 +γ of the n bits are uniformly random and the rest are fixed in advance. This improves upon previous constructions, which required that at least n/2 of the bits be random in order to extract many bits. Our construction also has applications in exposureresilient cryptography, giving explicit adaptive exposureresilient functions and, in turn, adaptive allornothing transforms. For sources where instead of bits the values are chosen from [d], for d>2, we give an algorithm that extracts a constant fraction of the randomness. We also give bounds on extracting randomness for sources where the fixed bits can depend on the random bits.