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An Improved Pseudo-random Generator Based on Discrete Log
[chapter]
2000
Lecture Notes in Computer Science
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Under the assumption that solving the discrete logarithm problem modulo an n-bit prime p is hard even when the exponent is a small c-bit number, we construct a new and improved pseudo-random bit generator ...
Using typical parameters, n = 1024 and c = 160, this yields roughly 860 pseudo-random bits per small exponentiations. ...
[10] show that if one considers discrete-log modulo a composite then almost n/2 pseudo-random bits can be extracted per modular exponentiation. ...
doi:10.1007/3-540-44598-6_29
fatcat:lixhzkeihndqljlkmz3c6agjeq
An efficient discrete log pseudo random generator
[chapter]
1998
Lecture Notes in Computer Science
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This leads to a very efficient pseudo-random number generator which produces n -c bits per iteration. ...
For example, when n = 1024 bits and c = 128 bits our pseudo-random number generator produces a tittle less than 900 bits per exponentiation. ...
s of size to(log n) is hard to invert.
5 Pseudo Random Bit Generator In this section we provide the details of the new pseudo-random bit generator. ...
doi:10.1007/bfb0055737
fatcat:lyrdtbkcovcvfdut3sb2eocfti
Pseudo-random Number Generation on the IBM 4758 Secure Crypto Coprocessor
[chapter]
2001
Lecture Notes in Computer Science
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In this paper we explore pseudo-random number generation on the IBM 4758 Secure Crypto Coprocessor. ...
In particular we compare several variants of Gennaro's provably secure generator, proposed at Crypto 2000, with more standard techniques based on the SHA-1 compression function. ...
Conclusions The results show that the SHA-1 based pseudorandom number generation is still considerably faster than the one based on discrete logarithms. ...
doi:10.1007/3-540-44709-1_9
fatcat:ddehnwo5gzg5bdhkdfhswjrgoa
Optimal Discrete Uniform Generation from Coin Flips, and Applications
[article]
2013
arXiv
pre-print
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This article introduces an algorithm to draw random discrete uniform variables within a given range of size n from a source of random bits. ...
I also provide a detailed analysis of the number of bits that are spent per variate, and offer some extensions and applications, in particular to the optimal random generation of permutations. ...
and Axel Bacher for a discussion on Lehmer codes. ...
arXiv:1304.1916v1
fatcat:yydtdcmnyrg6phinmn5aa2lk6e
Privately Outsourcing Exponentiation to a Single Server: Cryptanalysis and Optimal Constructions
[chapter]
2016
Lecture Notes in Computer Science
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a constant number of (generic) group operations. ...
We analyze the security of an efficient protocol for securely outsourcing multiexponentiations proposed at ESORICS 2014. ...
When the base u is fixed, one can assume that C can use a pseudo-random power generator for u. ...
doi:10.1007/978-3-319-45744-4_13
fatcat:rfdnfaoo2vehreq4cb4bpbzb5i
Quality Prediction and Yield Improvement in Process Manufacturing Based on Data Analytics
2020
Processes
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Finally, in Step 3, we predict quality values based on the data obtained in Step 1 and Step 2 and calculate yield values with the use of the predicted value. ...
However, it is more difficult to manage quality in continuous-flow manufacturing than in discrete manufacturing because partial defects can significantly affect the quality of an entire lot of final product ...
In Step 1, loss data were generated by random forest-based pseudo labeler. In Step 2, feature data were predicted with RNN. ...
doi:10.3390/pr8091068
fatcat:3kkqgm2urrhozgwnjtd4aixgau
Speeding up discrete log and factoring based schemes via precomputations
[chapter]
1998
Lecture Notes in Computer Science
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Our constructions use random walks on Cayley (expander) graphs over Abelian groups. Our analysis involves non-linear versions of lattice problems. ...
Our methods are novel in the sense that they identify and thoroughly exploit the randomness issues related to the instances generated in these public-key schemes. ...
Kannan for discussions on methods for attacking hidden subset sum problems. ...
doi:10.1007/bfb0054129
fatcat:smvgjccijrht3j63u3ca7dcbxm
A statistical analysis of probabilistic counting algorithms
[article]
2010
arXiv
pre-print
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We apply conventional statistical methods to compare probabilistic algorithms based on storing either selected order statistics, or random projections. ...
We present a statistical analysis of probabilistic counting algorithms, focusing on two techniques that use pseudo-random variates to form low-dimensional data sketches. ...
At time t, the data type i t is used as the seed of a random number generator to produce the first pseudo-random number h(i t ) uniformly distributed on (0,1). Write h(i t ) ∼ U(0, 1). ...
arXiv:0801.3552v3
fatcat:yhf6wknoofcphkwkxvqsw3eguq
Quasi-Random Sampling for Condensation
[chapter]
2000
Lecture Notes in Computer Science
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The problem of tracking pedestrians from a moving car is a challenging one. The Condensation tracking algorithm is appealing for its generality and potential for real-time implementation. ...
This paper presents an improved algorithm that addresses these problems by using a simplified motion model, and employing quasi-Monte Carlo techniques to efficiently sample the resulting tracking problem ...
a log-log scale (base 2). ...
doi:10.1007/3-540-45053-x_9
fatcat:jmdctuc7ajbvrcdtuio7xizn34
A kinetic triangulation scheme for moving points in the plane
2011
Computational geometry
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Thus, compared to the previous solution of Agarwal, Wang and Yu (2006) [4], we achieve a (slightly) improved bound on the number of discrete changes in the triangulation. ...
Our triangulation scheme experiences an expected number of O (n 2 β s+2 (n) log 2 n) discrete changes, and handles them in a manner that satisfies all the standard requirements from a kinetic data structure ...
Acknowledgements We thank the anonymous referees for valuable suggestions that helped us to improve the presentation. ...
doi:10.1016/j.comgeo.2010.11.001
fatcat:4bkhxtm6b5b5flanabhysxj5g4
Nearly One-Sided Tests and the Goldreich-Levin Predicate
[chapter]
2003
Lecture Notes in Computer Science
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We provide an efficient reduction establishing improved security for the Goldreich-Levin hard-core bit against nearly one-sided tests. ...
In particular, this applies to signature schemes that utilize a pseudo-random generator as a provider of randomness. ...
Blum and Micali showed how to construct a pseudo-random bit generator (PRBG) whose security is based on the hardness of solving the discrete logarithm problem. ...
doi:10.1007/3-540-39200-9_12
fatcat:w55xlk3a4jcf5nhjtk4d5fwciu
Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model
2001
Transportation Research Part B: Methodological
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We suggest and implement an alternative quasi-random maximum simulated likelihood method which uses cleverly crafted non-random but more uniformly distributed sequences in place of the pseudo-random points ...
Keywords: Mixed multinomial logit model, maximum simulated likelihood estimation, pseudorandom sequences, quasi-random sequences, polynomial-based cubature, discrete choice analysis. ...
The author would also like to thank Frank Koppelman and three referees for valuable comments on an earlier version of the paper. ...
doi:10.1016/s0191-2615(00)00014-x
fatcat:svvjrjtvzfax3ainvpldrst6ma
Nearly One-Sided Tests and the Goldreich?Levin Predicate
2003
Journal of Cryptology
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We provide an efficient reduction establishing improved security for the Goldreich-Levin hard-core bit against nearly one-sided tests. ...
In particular, this applies to signature schemes that utilize a pseudo-random generator as a provider of randomness. ...
Blum and Micali showed how to construct a pseudo-random bit generator (PRBG) whose security is based on the hardness of solving the discrete logarithm problem. ...
doi:10.1007/s00145-003-0141-4
fatcat:tksbhy5hrjagtlntmeqbw2oy4e
Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences
2003
Transportation Research Part B: Methodological
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Niederreiter, 1995 for a discussion of pseudo-random sequence generation). ...
However, rather than using pseudo-random sequences for the discrete points, the quasi-Monte Carlo approach uses "cleverly" crafted non-random and more uniformly distributed ...
Frank Koppelman, Kenneth Train, and an anonymous reviewer provided important suggestions for improvement on an earlier version of this paper. ...
doi:10.1016/s0191-2615(02)00090-5
fatcat:dy5lzxwqzzg3pf3wp4dbkeujou
Instantaneous Frequency Estimation Using Stochastic Calculus and Bootstrapping
2005
EURASIP Journal on Advances in Signal Processing
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An approximate expression for the Cramér-Rao lower bound is derived. An example is given, and a comparison to existing methods is provided. ...
Pseudo-maximum likelihood estimates are obtained through Girsanov theory and the Radon-Nikodym derivative. ...
( 1 ) 1 Discretize the form of the pseudo-log-likelihood function λ of (47) using the formulae in (80) and (81).(2) For a given γ, use (50) to find f .(3) For a given estimated f , use the simulation-based ...
doi:10.1155/asp.2005.1886
fatcat:7x6dptrnd5ehpbzpcafell2w4a
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