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OrderPreserving Symmetric Encryption
"... We initiate the cryptographic study of orderpreserving symmetric encryption (OPE), a primitive suggested in the database community by Agrawal et al. (SIGMOD ’04) for allowing efficient range queries on encrypted data. Interestingly, we first show that a straightforward relaxation of standard securi ..."
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Cited by 24 (0 self)
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We initiate the cryptographic study of orderpreserving symmetric encryption (OPE), a primitive suggested in the database community by Agrawal et al. (SIGMOD ’04) for allowing efficient range queries on encrypted data. Interestingly, we first show that a straightforward relaxation of standard security notions for encryption such as indistinguishability against chosenplaintext attack (INDCPA) is unachievable by a practical OPE scheme. Instead, we propose a security notion in the spirit of pseudorandom functions (PRFs) and related primitives asking that an OPE scheme look “asrandomaspossible ” subject to the orderpreserving constraint. We then design an efficient OPE scheme and prove its security under our notion based on pseudorandomness of an underlying blockcipher. Our construction is based on a natural relation we uncover between a random orderpreserving function and the hypergeometric probability distribution. In particular, it makes blackbox use of an efficient sampling algorithm for the latter. 1
A Rejection Technique for sampling from TConcave Distributions
 ACM Transactions on Mathematical Software
, 1994
"... : A rejection algorithm  called transformed density rejection  that uses a new method for constructing simple hat functions for an unimodal, bounded density f is introduced. It is based on the idea to transform f with a suitable transformation T such that T (f(x)) is concave. f is then called T ..."
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Cited by 19 (8 self)
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: A rejection algorithm  called transformed density rejection  that uses a new method for constructing simple hat functions for an unimodal, bounded density f is introduced. It is based on the idea to transform f with a suitable transformation T such that T (f(x)) is concave. f is then called T concave and tangents of T (f(x)) in the mode and in a point on the left and right side are used to construct a hat function with tablemountain shape. It is possible to give conditions for the optimal choice of these points of contact. With T = \Gamma1= p x the method can be used to construct a universal algorithm that is applicable to a large class of unimodal distributions including the normal, beta, gamma and tdistribution. AMS Subject Classification: 65C10, 68C25. CR Categories and Subject Descriptors: G.3 [Probability and Statistics]: Random number generation General Terms: Algorithms Additional Key Words and Phrases: Rejection method, logconcave distributions, universal method 1....
A Universal Generator for Discrete LogConcave Distributions
 Computing
, 1994
"... : We give an algorithm that can be used to sample from any discrete logconcave distribution (e.g. the binomial and hypergeometric distributions). It is based on rejection from a discrete dominating distribution that consists of parts of the geometric distribution. The algorithm is uniformly fast fo ..."
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Cited by 9 (3 self)
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: We give an algorithm that can be used to sample from any discrete logconcave distribution (e.g. the binomial and hypergeometric distributions). It is based on rejection from a discrete dominating distribution that consists of parts of the geometric distribution. The algorithm is uniformly fast for all discrete logconcave distributions and not much slower than algorithms designed for a single distribution. AMS Subject Classification: 65C10, 68C25. Key Words: Random number generation, logconcave distributions, rejection method, simulation. 1. Introduction A discrete distribution on the integers is called logconcave if the probabilities p k satisfy p 2 k p k\Gamma1 p k+1 for all k. Most of the classical discrete distributions like the binomial, Poisson, negative binomial and hypergeometric distributions are of this type (the only non logconcave distribution that has a chapter of its own in [9] is the logarithmic series distribution). Among the less often used logconcave di...
Sampling From Discrete And Continuous Distributions With CRand
 In Simulation and
, 1992
"... CRAND is a system of TurboC routines and functions intended for use on microcomputers. It contains uptodate random number generators for more than thirty univariate distributions. For some important distributions the user has the choice between extremely fast but rather complicated method ..."
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Cited by 5 (1 self)
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CRAND is a system of TurboC routines and functions intended for use on microcomputers. It contains uptodate random number generators for more than thirty univariate distributions. For some important distributions the user has the choice between extremely fast but rather complicated methods and somewhat slower but also much simpler procedures. Menu driven demo programs allow to test and analyze the generators with regard to speed and quality of the output. 1.
Monte Carlo Algorithms for HardyWeinberg Proportions
 Duke University
, 2003
"... this paper. Slow and fast direct generation of random variates Given that we derived in (2) from the uniform permutation on 2N alleles, we can generate a random variate directly from by rst generating a random permutation of the 2N alleles and then just counting the frequencies f ij directly. We ..."
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Cited by 5 (2 self)
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this paper. Slow and fast direct generation of random variates Given that we derived in (2) from the uniform permutation on 2N alleles, we can generate a random variate directly from by rst generating a random permutation of the 2N alleles and then just counting the frequencies f ij directly. We will refer to this as the naive Monte Carlo method for this problem. At rst glance this appears to be an eective method, but closer examination reveals that it is actually an exponential algorithm. Generation of the permutation of 2N elements requires 2N space and 2N draws of random uniforms. We are only interested in m choose 2 or (m ) dierent frequencies, but N might be exponentially large in the size of the problem instance. Hence directly forming the permutation is actually an exponential algorithm for the problem
The Patchwork Rejection Technique for Sampling from Unimodal Distributions
, 1998
"... We report on both theoretical developments of and computational experience with the patchwork rejection technique as studied in [20] and [21]. The basic approach is due to Minh [13] who suggested a special sampling method for the gamma distribution. Its general objective is to rearrange the area bel ..."
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Cited by 1 (0 self)
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We report on both theoretical developments of and computational experience with the patchwork rejection technique as studied in [20] and [21]. The basic approach is due to Minh [13] who suggested a special sampling method for the gamma distribution. Its general objective is to rearrange the area below the density or histogram f(x) in the body of the distribution by certain point reflections such that variates may be generated efficiently within a large center interval. This is carried out via uniform hat functions combined with minorizing rectangles for immediate acceptance of one transformed uniform deviate. The remaining tails of f(x) are covered by exponential functions. Experiments show that patchwork rejection algorithms are in general faster than its competitors at the cost of higher setup times. Categories and Subject Descriptors: G.3 [Probability and Statistics]: Random number generation; G.3 [Probability and Statistics]: Statistical Software; I.6.1 [Simulation and Modeling ]: Simulation Theory General Terms: Random Variate Transformations, Algorithms, Stochastic Simulation Additional Key Words and Phrases: Random variate generation, patchwork rejection, sampling techniques, unimodal distributions 1
Table of Contents
, 2003
"... Intel products are not intended for use in medical, life saving, life sustaining, critical control or safety systems, or in nuclear facility applications. Intel may make changes to specifications and product descriptions at any time, without notice. ..."
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Intel products are not intended for use in medical, life saving, life sustaining, critical control or safety systems, or in nuclear facility applications. Intel may make changes to specifications and product descriptions at any time, without notice.
SALE AND/OR USE OF INTEL PRODUCTS INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR
, 2005
"... for any errors or inaccuracies that may appear in this document or any software that may be provided in association with this document. This document and the software described in it are furnished under license and may only be used or copied in accordance with the terms of the license. No license, e ..."
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for any errors or inaccuracies that may appear in this document or any software that may be provided in association with this document. This document and the software described in it are furnished under license and may only be used or copied in accordance with the terms of the license. No license, express or implied, by estoppel or otherwise, to any intellectual property
3.0 Documents Intel Math Kernel Library release 6.1. 07/03 4.0 Documents Intel Math Kernel Library release 7.0 Beta. 11/03 5.0 Documents Intel Math Kernel Library release 7.0 Gold. 04/04 6.0 Documents Intel Math Kernel Library release 7.0.1. 07/04 7.0 Doc
"... The information in this document is subject to change without notice and Intel Corporation assumes no responsibility or liability for any errors or inaccuracies that may appear in this document or any software that may be provided in association with this document. This document and the software des ..."
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The information in this document is subject to change without notice and Intel Corporation assumes no responsibility or liability for any errors or inaccuracies that may appear in this document or any software that may be provided in association with this document. This document and the software described in it are furnished under license and may only be used or copied in accordance with the terms of the license. No license, express or implied, by estoppel or otherwise, to any intellectual property rights is granted by this document. The information in this document is provided in connection with Intel products and should not be construed as a commitment by Intel Corporation.
Contents
, 2009
"... This package provides tools to compute densities, mass functions, distribution functions and their inverses, and reliability functions, for various continuous and discrete probability distributions. It also offers facilities for estimating the parameters of some distributions from ..."
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This package provides tools to compute densities, mass functions, distribution functions and their inverses, and reliability functions, for various continuous and discrete probability distributions. It also offers facilities for estimating the parameters of some distributions from