# Random Number Generators—Principles and Practices: A Guide for Engineers and Programmers

Walter de Gruyter GmbH & Co KG, Sep 10, 2018 - Computers - 439 pages
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Random Number Generators, Principles and Practices has been written for programmers, hardware engineers, and sophisticated hobbyists interested in understanding random numbers generators and gaining the tools necessary to work with random number generators with confidence and knowledge.

Using an approach that employs clear diagrams and running code examples rather than excessive mathematics, random number related topics such as entropy estimation, entropy extraction, entropy sources, PRNGs, randomness testing, distribution generation, and many others are exposed and demystified.

If you have ever

• Wondered how to test if data is really random
• Needed to measure the randomness of data in real time as it is generated
• Wondered how to get randomness into your programs
• Wondered whether or not a random number generator is trustworthy
• Wanted to be able to choose between random number generator solutions
• Needed to turn uniform random data into a different distribution
• Needed to ensure the random numbers from your computer will work for your cryptographic application
• Wanted to combine more than one random number generator to increase reliability or security
• Wanted to get random numbers in a floating point format
• Needed to verify that a random number generator meets the requirements of a published standard like SP800-90 or AIS 31
• Needed to choose between an LCG, PCG or XorShift algorithm

Then this might be the book for you.

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### Contents

 1 Introduction 1 2 Entropy Sources 23 3 Entropy Extraction 41 4 Cryptographically Secure Pseudorandom Number Generators 95 5 Nondeterministic Random Number Generators 119 6 Statistically Uniform Noncryptographic PRNGs 123 7 Gaussian or Normally Distributed PRNGs 137 8 Testing Random Numbers 145
 15 Making a Uniform Random Number Between Nonpower of Two Bounds 329 16 Generating Random Prime Numbers 337 17 Additive Distributions 345 18 Probability Distributions 349 19 Quantifying Entropy 383 20 Random Methods to Generate π 393 Appendix A Adaptive Proportion Test Cutoff Tables 397 Appendix B HighPrecision Incomplete Beta Function Implementation 403

 9 Online Random Number Testing 173 10 SP80022 Distinguishability Tests 199 11 Software Tools 251 12 RdRand and RdSeed Instructions in x86 CPUs 289 13 Accessing RNGs from Software 295 14 FloatingPoint Random Numbers 317
 Appendix C Incomplete Gamma Function Implementation 409 Appendix D Software Tool Sources 415 Appendix E Listing Reference 417 Bibliography 421 Index 423 Copyright