Dieharder: A Random Number Test Suite - Robert G. Brown's General Tools Page
Welcome to the dieharder distribution website. Version 3.29.4beta is the current snapshot. Some of the documentation below may not quite be caught up to it, but it should be close. Dieharder is a random number generator (rng) testing suite. It is intended to test generators, not files of possibly random numbers as the latter is a fallacious view of what it means to be random. Is the number 7 rando
Better Random Number Generation for OpenSSL, libc, and Linux Mainline | Amazon Web Services
AWS Open Source Blog Better Random Number Generation for OpenSSL, libc, and Linux Mainline 中文版 In 2015, AWS introduced s2n, a new open source implementation of the TLS/SSL protocols that protect the privacy and integrity of data moving over a network. s2n was designed to be secure, simple, small, and fast. The project is thriving, and we use it extensively. In February, our CISO Stephen Schmidt sh
Interoperable Randomness Beacons | CSRC
NIST Randomness Beacon (Version 2.0 Beta) -- work in progress WARNING: DO NOT USE BEACON GENERATED VALUES AS SECRET CRYPTOGRAPHIC KEYS. This prototype implementation generates full-entropy bit-strings and posts them in blocks of 512 bits every 60 seconds. Each such value is sequence-numbered, time-stamped and signed, and includes the hash of the previous value to chain the sequence of values toget
xoshiro/xoroshiro generators and the PRNG shootout
Introduction This page describes some new pseudorandom number generators (PRNGs) we (David Blackman and I) have been working on recently, and a shootout comparing them with other generators. Details about the generators can be found in our paper. Information about my previous xorshift-based generators can be found here, but they have been entirely superseded by the new ones, which are faster and b
PCG, A Family of Better Random Number Generators
* For the PCG family, arbitrary k-dimensional equidistribution (and the huge periods it implies) requires PCG's extended generation scheme. † ChaCha entry based on an optimized C++ implementation of ChaCha, kindly provided by Orson Peters. Random Number Generation Is Important Algorithmic random number generators are everywhere, used for all kinds of tasks, from simulation to computational creativ
A fast alternative to the modulo reduction
Suppose you want to pick an integer at random in a set of N elements. Your computer has functions to generate random 32-bit integers, how do you transform such numbers into indexes no larger than N? Suppose you have a hash table with a capacity N. Again, you need to transform your hash values (typically 32-bit or 64-bit integers) down to an index no larger than N. Programmers often get around this
Myths about /dev/urandom - Thomas' Digital Garden
Myths about /dev/urandom¶ There are a few things about /dev/urandom and /dev/random that are repeated again and again. Still they are false. By the way I'm mostly talking about reasonably recent Linux systems, not other UNIX-like systems. /dev/urandom is insecure. Always use /dev/random for cryptographic purposes. Fact: /dev/urandom is the preferred source of cryptographic randomness on UNIX-like
せっき～のゲーム屋さん ゲーム世界を動かすサイコロの正体 ～ 往年のナムコタイトルから学ぶ乱数の進化と応用
[PR]上記の広告は3ヶ月以上新規記事投稿のないブログに表示されています。新しい記事を書く事で広告が消えます。 2014年9月4日 にあった、ＣＥＤＥＣの講演についての記事です。 ゲーム世界を動かすサイコロの正体 ～ 往年のナムコタイトルから学ぶ乱数の進化と応用 バンダイナムコのプログラマさんより乱数の講演です。 ・ゲームで使われている主要な乱数のしくみについて ・乱数のアルゴリズムが改良されていく歴史 ・乱数の選択の注意点 などについて、取り上げられました。 （乱数分布などは せっかくなので 初め、自分で乱数プログラム組んで 用意しようかと思ったのですが 力尽きてしまい・・・ 撮影した写真を使わせていただきました） -------------------------------------------------------------------- ●はじめに ・真の乱数 → 実際のサ
Surviving a bad RNG
A Few Thoughts on Cryptographic Engineering Some random thoughts about crypto. Notes from a course I teach. Pictures of my dachshunds. I'm a cryptographer and professor at Johns Hopkins University. I've designed and analyzed cryptographic systems used in wireless networks, payment systems and digital content protection platforms. In my research I look at the various ways cryptography can be used t
haveged - a simple entropy daemon
haveged - A simple entropy daemon Introduction The haveged project is an attempt to provide an easy-to-use, unpredictable random number generator based upon an adaptation of the HAVEGE algorithm. Haveged was created to remedy low-entropy conditions in the Linux random device that can occur under some workloads, especially on headless servers. Current development of haveged is directed towards impr
Cryptographic Random Numbers
I've written an article on Secure Random Numbers for the August 2002 issue of Windows Developer Magazine. During the research phase, I found that while a lot of resources were available online (mostly in the form of link farms), it was difficult to decide what to read and what not. I ended up reading most of what I found. To make the job easier for people trying to learn about secure random number
Fisher–Yates shuffle - Wikipedia
Example of shuffling five letters using Durstenfeld's in-place version of the Fisher–Yates shuffle The Fisher–Yates shuffle is an algorithm for shuffling a finite sequence. The algorithm takes a list of all the elements of the sequence, and continually determines the next element in the shuffled sequence by randomly drawing an element from the list until no elements remain.[1] The algorithm produc
Reservoir sampling - Wikipedia
Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot
良い乱数・悪い乱数
Ｃ言語標準ライブラリの乱数rand( )は質に問題があり、禁止している学会もある。 他にも乱数には様々なアルゴリズムがあるが、多くのものが問題を持っている。 最も多くの人に使われている乱数であろう Visual Basic の Rnd の質は最低である。 そもそも乱数とは 乱数とは、本来サイコロを振って出る目から得られるような数を意味する。 このような乱数は予測不能なものである。 しかし、計算機を使って乱数を発生させた場合、 次に出る数は完全に決まっているので、予測不能とはいえない。 そこで、計算機で作り出される乱数を疑似乱数(PRNG)と呼び区別することがある。 ここでは、特にことわらない限り乱数とは疑似乱数のことを指すとする。 計算機でソフト的に乱数を発生させることの最大のメリットは、 再現性があることである。 初期状態が同じであれば、発生する乱数も全く同じものが得られる。 このことは
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Efficiently Generating a Number in a Range | PCG, A Better Random Number Generator
GitHub - google/randen: Fast backtracking-resistant random generator: https://arxiv.org/abs/1810.02227
frandom
cr.yp.to: 2017.07.23: Fast-key-erasure random-number generators
The Reasonable Effectiveness of the Multiplicative Weights Update Algorithm
GitHub - jedisct1/libsodium: A modern, portable, easy to use crypto library.
