Pseudorandom number generators
A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by an initial value, called the PRNG's seed (which may include truly random values). Although sequences that are closer to truly random can be generated using hardware random number generators, pseudorandom number generators are important in practice for their speed in number generation and their reproducibility. PRNGs are central in applications such as simulations (e.g. for the Monte Carlo method), electronic games (e.g. for procedural generation), and cryptography. Cryptographic applications require the output not to be predictable from earlier outputs, and more elaborate algorithms, which do not inherit the linearity of simpler PRNGs, are needed. Good statistical properties are a central requirement for the output of a PRNG. In general, careful mathematical analysis is required to have any confidence that a PRNG generates numbers that are sufficiently close to random to suit the intended use. John von Neumann cautioned about the misinterpretation of a PRNG as a truly random generator, joking that "Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin." (Wikipedia).
Pseudorandom Number Generation and Stream Ciphers
Fundamental concepts of Pseudorandom Number Generation are discussed. Pseudorandom Number Generation using a Block Cipher is explained. Stream Cipher & RC4 are presented.
From playlist Network Security
Pseudorandom Generators for Regular Branching Programs - Amir Yehudayoff
Amir Yehudayoff Institute for Advanced Study March 16, 2010 We shall discuss new pseudorandom generators for regular read-once branching programs of small width. A branching program is regular if the in-degree of every vertex in it is (either 0 or) 2. For every width d and length n, the p
From playlist Mathematics
Pseudorandom Generators for Read-Once ACC^0 - Srikanth Srinivasan
Srikanth Srinivasan DIMACS April 24, 2012 We consider the problem of constructing pseudorandom generators for read-once circuits. We give an explicit construction of a pseudorandom generator for the class of read-once constant depth circuits with unbounded fan-in AND, OR, NOT and generaliz
From playlist Mathematics
How to Generate Pseudorandom Numbers | Infinite Series
Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: https://to.pbs.org/donateinfi What is a the difference between a random and a pseudorandom number? And what can pseudo random numbers allow us to do that random numbers can't? Tweet at us! @pbsinfi
From playlist Probability
Pseudorandom Generators for CCO[p]CCO[p] and the Fourier Spectrum... - Shachar Lovett
Shachar Lovett Institute for Advanced Study October 5, 2010 We give a pseudorandom generator, with seed length O(logn)O(logn), for CC0[p]CC0[p], the class of constant-depth circuits with unbounded fan-in MODpMODp gates, for prime pp. More accurately, the seed length of our generator is O(
From playlist Mathematics
Coding Math: Episode 51 - Pseudo Random Number Generators Part I
Back to School Special. This short series will discuss pseudo random number generators (PRNGs), look at how they work, some algorithms for PRNGs, and how they are used. Support Coding Math: http://patreon.com/codingmath Source Code: https://jsbin.com/nifutup/1/edit?js,output Earlier Sourc
From playlist Episodes
Coding Math: Episode 52 - Pseudo Random Number Generators, Part II
This time we look at a couple of existing PRNG libraries available in JavaScript, and look at some examples of how PRNGs can be used in cryptography, games, and generative art. Support Coding Math: http://patreon.com/codingmath Source Code: Crypto: http://jsbin.com/kipequk/2/edit?js,cons
From playlist Episodes
Better Pseudorandom Generators from Milder Pseudorandom Restrictions - Parikshit Gopalan
Parikshit Gopalan Microsoft Research Silicon Valley, Mountain View, CA April 3, 2012 We present an iterative approach to constructing pseudorandom generators, based on the repeated application of mild pseudorandom restrictions. We use this template to construct pseudorandom generators for
From playlist Mathematics
Jonathan Katz - Introduction to Cryptography Part 1 of 3 - IPAM at UCLA
Recorded 25 July 2022. Jonathan Katz of the University of Maryland presents "Introduction to Cryptography I" at IPAM's Graduate Summer School Post-quantum and Quantum Cryptography. Abstract: This lecture will serve as a "crash course" in modern cryptography for those with no prior exposure
From playlist 2022 Graduate Summer School on Post-quantum and Quantum Cryptography
Pseudorandom number generators | Computer Science | Khan Academy
Random vs. Pseudorandom Number Generators Watch the next lesson: https://www.khanacademy.org/computing/computer-science/cryptography/modern-crypt/v/the-fundamental-theorem-of-arithmetic-1?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Missed the previous lesson? https://www.k
From playlist Journey into cryptography | Computer Science | Khan Academy
Patrick Morris - Triangle factors in pseudorandom graphs (CMSA Combinatorics Seminar)
Patrick Morris presents "Triangle factors in pseudorandom graphs," 31st March 2021 (CMSA Combinatorics Seminar) http://combinatorics-australasia.org/seminars.html
From playlist CMSA Combinatorics Seminar
Giray Ökten: Number sequences for simulation - lecture 1
After an overview of some approaches to define random sequences, we will discuss pseudorandom sequences and low-discrepancy sequences. Applications to numerical integration, Koksma-Hlawka inequality, and Niederreiter’s uniform point sets will be discussed. We will then present randomized q
From playlist Probability and Statistics
Interview Igor Shparlinski : Jean Morlet Chair (First Semester 2014)
Jean-Morlet Chair on 'Number Theory and its Applications to Cryptography' Beneficiaries : Jean-Morlet Chair : Igor SHPARLINSKI School of Mathematics and Statistics University of New South Wales Sydney, Australia igor.shparlinski@unsw.edu.au Local project leader : David KOHEL I2M - Insti
From playlist Jean-Morlet Chair's holders - Interviews
Periodic Random Function Generation using Matplotlib and Python
In signal processing, for certain applications, periodic random signals might be needed. In this micro-tutorial we show how such a periodic random function can be generated. This is achieved by looping a 2d random noise onto itself to create a 1 dimensional random noise. The algorithm is i
From playlist Engineering Animations
Cryptography and Network Security by Prof. D. Mukhopadhyay, Department of Computer Science and Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
From playlist Computer - Cryptography and Network Security
Clip 4/6 Speaker: Travis Goodspeed Open JTAG with Voltage Glitching The GoodFET is an open source tool for programming microcontrollers and memories by SPI, I2C, JTAG, and a slew of vendor-proprietary protocols. In this lecture, the design of the GoodFET will be explained in detail,
From playlist 26C3: Here be dragons day 2