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Pseudorandom number generators | Computer Science | Khan Academy
 
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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.khanacademy.org/computing/computer-science/cryptography/crypt/v/perfect-secrecy?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information). About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 157170 Khan Academy Labs
cryptography - Pseudorandom Generators
 
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Cryptography To get certificate subscribe: https://www.coursera.org/learn/cryptography ======================== Playlist URL: https://www.youtube.com/playlist?list=PL2jykFOD1AWb07OLBdFI2QIHvPo3aTTeu ============================ Youtube channel: https://www.youtube.com/user/intrigano ============================ https://scsa.ge/en/online-courses/ https://www.facebook.com/cyberassociation/
Views: 1923 intrigano
Applied Cryptography: Random Numbers (1/2)
 
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Previous video: https://youtu.be/6ro3z2pTiqI Next video: https://youtu.be/KuthrX4G1ss
Views: 4039 Leandro Junes
Pseudo Random Number Generator - Applied Cryptography
 
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This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 8265 Udacity
Pseudo Random Number Generators (CSS322, Lecture 7, 2013)
 
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Pseudo random number generators; Linear Congruential Generator. Lecture 7 of CSS322 Security and Cryptography at Sirindhorn International Institute of Technology, Thammasat University. Given on 12 December 2013 at Bangkadi, Pathumthani, Thailand by Steven Gordon. Course material via: http://sandilands.info/sgordon/teaching
Views: 21215 Steven Gordon
How to Generate Pseudorandom Numbers | Infinite Series
 
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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! @pbsinfinite Facebook: facebook.com/pbsinfinite series Email us! pbsinfiniteseries [at] gmail [dot] com Previous Episode How many Cops to catch a Robber? | Infinite Series https://www.youtube.com/watch?v=fXvN-pF76-E Computers need to have access to random numbers. They’re used to encrypt information, deal cards in your game of virtual solitaire, simulate unknown variables -- like in weather prediction and airplane scheduling, and so much more. But How can a computer possibly produce a random number? Written and Hosted by Kelsey Houston-Edwards Produced by Rusty Ward Graphics by Ray Lux Assistant Editing and Sound Design by Mike Petrow Made by Kornhaber Brown (www.kornhaberbrown.com) Special Thanks to Alex Townsend Big thanks to Matthew O'Connor and Yana Chernobilsky who are supporting us on Patreon at the Identity level! And thanks to Nicholas Rose and Mauricio Pacheco who are supporting us at the Lemma level!
Views: 103142 PBS Infinite Series
cryptography - Pseudorandom Functions and Block Ciphers
 
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Cryptography To get certificate subscribe: https://www.coursera.org/learn/cryptography ======================== Playlist URL: https://www.youtube.com/playlist?list=PL2jykFOD1AWb07OLBdFI2QIHvPo3aTTeu ============================ Youtube channel: https://www.youtube.com/user/intrigano ============================ https://scsa.ge/en/online-courses/ https://www.facebook.com/cyberassociation/
Views: 2732 intrigano
CSE571-11-07: Pseudorandom Number Generation and Stream Ciphers
 
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Audio/Video Recording of Professor Raj Jain's class lecture on Pseudorandom Number Generation and Stream Ciphers. It covers Pseudo Random Numbers, A Sample Generator, Terminology, Linear-Congruential Generators, Blum Blum Shub Generator, Random & Pseudorandom Number Generators, Using Block Ciphers as PRNGs, ANSI X9.17 PRG, Natural Random Noise, Stream Ciphers, RC4, RC4 Key Schedule, RC4 Encryption, RC4
Views: 4629 Raj Jain
Coding Math: Episode 51 - Pseudo Random Number Generators Part I
 
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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 Source Code: http://github.com/bit101/codingmath
Views: 24098 Coding Math
Pseudo Random Numbers and Stream Ciphers (CSS322, L9, Y14)
 
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Pseudo random number generators; stream ciphers. Course material via: http://sandilands.info/sgordon/teaching
Views: 2228 Steven Gordon
Is Anything Truly Random?
 
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In 2012, scientists developed a system to predict what number a rolled die would land on. Is anything truly random or is it all predictable? Can Game Theory Help A Presidential Candidate Win? - http://bit.ly/2bMqILU Sign Up For The Seeker Newsletter Here - http://bit.ly/1UO1PxI Read More: On Fair And Randomness http://www.sciencedirect.com/science/article/pii/S0890540109001369 "We investigate the relation between the behavior of non-deterministic systems under fairness constraints, and the behavior of probabilistic systems. To this end, first a framework based on computable stopping strategies is developed that provides a common foundation for describing both fair and probabilistic behavior. On the basis of stopping strategies it is then shown that fair behavior corresponds in a precise sense to random behavior in the sense of Martin-Löf's definition of randomness." Predicting A Die Throw http://phys.org/news/2012-09-die.html "Vegas, Monte Carlo, and Atlantic City draw people from around the world who are willing to throw the dice and take their chances. Researchers from the Technical University of Lodz, Poland, have spotted something predictable in the seemingly random throw of the dice." HTG Explains: How Computers Generate Random Numbers http://www.howtogeek.com/183051/htg-explains-how-computers-generate-random-numbers/ "Computers generate random number for everything from cryptography to video games and gambling. There are two categories of random numbers - "true" random numbers and pseudorandom numbers - and the difference is important for the security of encryption systems." ____________________ DNews is dedicated to satisfying your curiosity and to bringing you mind-bending stories & perspectives you won't find anywhere else! New videos daily. Watch More DNews on Seeker http://www.seeker.com/show/dnews/ Subscribe now! http://www.youtube.com/subscription_center?add_user=dnewschannel DNews on Twitter http://twitter.com/dnews Trace Dominguez on Twitter https://twitter.com/tracedominguez DNews on Facebook https://facebook.com/DiscoveryNews DNews on Google+ http://gplus.to/dnews Discovery News http://discoverynews.com Sign Up For The Seeker Newsletter Here: http://bit.ly/1UO1PxI Special thanks to Jules Suzdaltsev for hosting DNews! Check Jules out on Twitter: https://twitter.com/jules_su
Views: 184682 Seeker
What is PSEUDORANDOM NUMBER GENERATOR? What does PSEUDORANDOM NUMBER GENERATOR mean?
 
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What is PSEUDORANDOM NUMBER GENERATOR? What does PSEUDORANDOM NUMBER GENERATOR mean? PSEUDORANDOM NUMBER GENERATOR meaning - PSEUDORANDOM NUMBER GENERATOR definition - PSEUDORANDOM NUMBER GENERATOR explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. 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 a relatively small set of initial values, 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, and joked that "Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin." A PRNG can be started from an arbitrary initial state using a seed state. It will always produce the same sequence when initialized with that state. The period of a PRNG is defined thus: the maximum, over all starting states, of the length of the repetition-free prefix of the sequence. The period is bounded by the number of the states, usually measured in bits. However, since the length of the period potentially doubles with each bit of "state" added, it is easy to build PRNGs with periods long enough for many practical applications. If a PRNG's internal state contains n bits, its period can be no longer than 2n results, and may be much shorter. For some PRNGs, the period length can be calculated without walking through the whole period. Linear Feedback Shift Registers (LFSRs) are usually chosen to have periods of exactly 2n-1. Linear congruential generators have periods that can be calculated by factoring. Although PRNGs will repeat their results after they reach the end of their period, a repeated result does not imply that the end of the period has been reached, since its internal state may be larger than its output; this is particularly obvious with PRNGs with a one-bit output. Most PRNG algorithms produce sequences which are uniformly distributed by any of several tests. It is an open question, and one central to the theory and practice of cryptography, whether there is any way to distinguish the output of a high-quality PRNG from a truly random sequence, knowing the algorithms used, but not the state with which it was initialized. The security of most cryptographic algorithms and protocols using PRNGs is based on the assumption that it is infeasible to distinguish use of a suitable PRNG from use of a truly random sequence. The simplest examples of this dependency are stream ciphers, which (most often) work by exclusive or-ing the plaintext of a message with the output of a PRNG, producing ciphertext. The design of cryptographically adequate PRNGs is extremely difficult, because they must meet additional criteria (see below). The size of its period is an important factor in the cryptographic suitability of a PRNG, but not the only one. A PRNG suitable for cryptographic applications is called a cryptographically secure PRNG (CSPRNG). A requirement for a CSPRNG is that an adversary not knowing the seed has only negligible advantage in distinguishing the generator's output sequence from a random sequence. In other words, while a PRNG is only required to pass certain statistical tests, a CSPRNG must pass all statistical tests that are restricted to polynomial time in the size of the seed. Though a proof of this property is beyond the current state of the art of computational complexity theory, strong evidence may be provided by reducing the CSPRNG to a problem that is assumed to be hard, such as integer factorization. In general, years of review may be required before an algorithm can be certified as a CSPRNG.
Views: 2933 The Audiopedia
Section 4.5 Pseudorandom Numbers
 
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I do an example of finding pseudorandom numbers.
Views: 572 Michael Venn
LFSR 1
 
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Views: 26083 Jeff Suzuki
Proofs in Cryptography: Lecture 5 Pseudo Random Generators
 
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Proofs in Cryptography Lecture 5 Pseudo Random Generators ALPTEKİN KÜPÇÜ Assistant Professor of Computer Science and Engineering Koç University http://crypto.ku.edu.tr
Views: 2659 KOLT KU
Cryptography stream ciphers and pseudo random generators
 
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Cryptography Stream ciphers and pseudo random generators To get certificate subscribe: https://www.coursera.org/learn/crypto Playlist URL: https://www.youtube.com/playlist?list=PL2jykFOD1AWYosqucluZghEVjUkopdD1e About this course: Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key.
Views: 449 intrigano
Lecture 3: Stream Ciphers, Random Numbers and the One Time Pad by Christof Paar
 
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For slides, a problem set and more on learning cryptography, visit www.crypto-textbook.com
Pseudo Random Number Generators - Peter Faiman
 
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Peter Faiman White Hat VP, talks about pseudo-random number generators (PRNGs), random number quality, and the importance of unpredictable random numbers to cryptography.
Views: 2987 White Hat Cal Poly
Pseudorandom Generators I
 
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Raghu Meka, UCLA https://simons.berkeley.edu/talks/pseudorandom-generators-1 Pseudorandomness Boot Camp
Views: 930 Simons Institute
Pseudo Random Number Generator Solution - Applied Cryptography
 
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This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 2642 Udacity
6.875 (Cryptography) L6: Pseudorandom Generators
 
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Spring 2018 Cryptography & Cryptanalysis Prof. Vinod Vaikuntanathan
Views: 147 Andrew Xia
Coding Math: Episode 52 - Pseudo Random Number Generators, Part II
 
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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,console Landscape: http://jsbin.com/zizeje/1/edit?js,output Circles: http://jsbin.com/zizeje/2/edit?js,output
Views: 5482 Coding Math
Applied Cryptography: Random Numbers (2/2)
 
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Previous video: https://youtu.be/g3iH74XFaT0 Next video:
Views: 1344 Leandro Junes
PRNG Part 1
 
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Part 1 of a 3 part lesson on Pseudo Random Number Generators (PRNGs)
Pseudo Random Number Generators (CSS441, L08, Y15)
 
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True and pseudo random numbers; Linear Congruential Generator. Course material via: http://sandilands.info/sgordon/teaching
Views: 3152 Steven Gordon
Arduino Pseudo Random Non-Consecutive Number Generator
 
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*Click Below to Sign up for the free Arduino Video Course:* http://bit.ly/Arduino_Course *Click Below to Check Out the Premium Arduino Video Course:* http://bit.ly/Premium_Arduino *Click Below to Read About This Topic on Our Website* http://bit.ly/Random_Arduino *Description:* In this video we demonstrate how to create pseudo random numbers with Arduino - with a useful twist. This lesson was inspired by the following viewer question: "How do I create Random Non-Consecutive numbers with Arduino. P.S. These are the best tutorials that a complete idiot like you could ever make, thanks." -Anonymous *Let's overview exactly what we will talk about in todays episode:* Talk about pseudo random numbers. Identify the problem - using an Arduino sketch to demonstrate. Discuss how we might solve the problem. Write an Arduino sketch that solves the problem. Review what we talked about. *Pseudo Random Numbers* Before we answer the viewer’s question it is important to talk about what a pseudo random number is. A purely random number in the mathematical sense can't be predicted. The microcontroller that the Arduino uses (and for that case, most computers in general) can't really create pure random numbers. What they create instead are called pseudo random numbers. These are numbers that appear to be randomly generated, but if studied over time a predictable pattern emerges. The bottom line is that the random numbers we create with Arduino can be predicted. Now there are clever ways to create pseudo random numbers that act like the real deal – you can learn about one method in our video tutorial talking all about random numbers – but for this discussion, let’s return to our viewers inquiry. *Identify the Viewer’s Problem - use an Arduino sketch to demonstrate.* Ok, so let's go back to the viewers question, he wants to generate random numbers, but he never wants the same number generated two times in a row. Let's write an Arduino Sketch to make this clear. //This sketch outputs pseudo random integers. //A variable to hold pseudo random integers. int randomInt = 0; void setup() { //Initiate serial communication. Serial.begin(9600); }//Close setup function void loop() { //Create a random number and assign it to the randomInt variable. randomInt = random(0, 10); //Send randomInt to the serial port for displaying on the serial monitor window. Serial.print(randomInt); }//Close loop function. In the first block of code a variable that will hold the pseudo random integers is declared and initialized. //A variable to hold pseudo random integers. int randomInt = 0; In the setup() function we begin serial communication in order to display the numbers we generate on a computer display. void setup() { //Initiate serial communication. Serial.begin(9600); }//Close setup function In the loop() we create the random number with the Arduino random() function and assign the output to the variable we had just created. The random() function can take two arguments 1) the minimum value of the number we want generated 2) the maximum value we want generated. //Create a random number and assign it to the randomInt variable. randomInt = random(0, 10); I will use 0 for the minimum, and 10 for the maximum. Every time through the loop, a new random number will be assigned the randomInt variable. Finally, the value of randomInt is sent over the serial port to be displayed in the serial monitor window. //Send randomInt to the serial port for displaying on the serial monitor window. Serial.print(randomInt); If you upload this code and open the serial monitor you will see in some cases where the same number shows up two times in a row. This is the problem. The viewer doesn't ever want the same number two times in a row. *Discuss how we might solve the problem.* So let's talk about how we might solve this problem. We know we need to generate a random number. What if we create a variable to track the previous random number? Then we could use a condition that says something like "If the previous random number is equal to the random number that was just generated, toss that number out the window, and create a different one.” The final thing we would need to do is set the previous random number equal to the new random number, that way we keep updating our previous random number every time through the loop(). *Let’s Implement our solution in an Arduino Sketch.* Copy and paste this code into your Arduino IDE. All you need is an Arduino board attached to your computer to make it work. *Get the Code from the below address* http://bit.ly/Random_Arduino *About Us:* This Arduino tutorial was created by Open Source Hardware Group. We are an education company who seek to help people learn about electronics and programming through the ubiquitous Arduino development board.
True Random Number Generators - FST-01 - Well Tempered Hacker
 
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Randomness forms the basis of cryptography but computers are deterministic and therefore terrible for generating true randomness. In this episode we'll look at the FST-01, a $35 USB based true random number generator (TRNG) which harvests randomness from the environment. We'll flash the NeuG random number generator software onto the device using a serial programmer and a few wires. Then we'll plug it in, start it up and look at the random data it generates. Hardware: http://www.seeedstudio.com/wiki/FST-01 http://www.seeedstudio.com/depot/s/fst-01.html Software: http://www.gniibe.org/memo/development/gnuk/rng/neug.html #crypto #cryptography #random #randomnumber #geigercounter #cryptography #mouse #pgp #privatekey #flyingstonetiny #FST-01 #randomnumbergenerator #environment #computing #communication #messaging #mail #email
Views: 12902 Anders Brownworth
Cryptographically secure pseudorandom number generator
 
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Cryptographically secure pseudorandom number generator A cryptographically secure pseudo-random number generator (CSPRNG) or cryptographic pseudo-random number generator (CPRNG) is a pseudo-random number generator (PRNG) with properties that make it suitable for use in cryptography.Many aspects of cryptography require random numbers, for example: key generation. -Video is targeted to blind users Attribution: Article text available under CC-BY-SA image source in video https://www.youtube.com/watch?v=NL-EL2KcU-Q
Views: 788 WikiAudio
Pseudorandom Number Generation and Stream Ciphers
 
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Fundamental concepts of Pseudorandom Number Generation are discussed. Pseudorandom Number Generation using a Block Cipher is explained. Stream Cipher & RC4 are presented.
Views: 1239 Scholartica Channel
Cryptographically secure pseudorandom number generator Top # 7 Facts
 
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Cryptographically secure pseudorandom number generator Top # 7 Facts
Views: 82 Duryodhan Trivedi
The Lava Lamps That Help Keep The Internet Secure
 
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At the headquarters of Cloudflare, in San Francisco, there's a wall of lava lamps: the Entropy Wall. They're used to generate random numbers and keep a good bit of the internet secure: here's how. Thanks to the team at Cloudflare - this is not a sponsored video, they just had interesting lava lamps! There's a technical rundown of the system on their blog here: https://blog.cloudflare.com/lavarand-in-production-the-nitty-gritty-technical-details Edited by Michelle Martin, @mrsmmartin I'm at http://tomscott.com on Twitter at http://twitter.com/tomscott on Facebook at http://facebook.com/tomscott and on Snapchat and Instagram as tomscottgo
Views: 1254196 Tom Scott
How Machines Generate Random Numbers with Time
 
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Pseudorandom number generators are explained using John Von Neumann's middle squares method. Machines can't roll dice so they do a trick to generate randomness - they grow randomness. The middle squares method is explained from a computer science perspective using clocks as seeds. This is a clip from Art of the Problem episode #1. This clip features original music from Hannah Addario-Berry
Views: 38766 Art of the Problem
Math for Game Developers - Making Randomness (Pseudorandom Number Generators)
 
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How random number generators work and how to get good numbers out of them. Find the source code here: https://github.com/BSVino/MathForGameDevelopers/tree/probability-random New video every Thursday. Question? Leave a comment below, or ask me on Twitter: https://twitter.com/VinoBS EXERCISES: 1. Modify the function to pass the current time into the random number seed and verify that a new sequence is always produced. 2. Create a pseudorandom number generator that generates only 1's and 0's, false and true values. 3. How would the difference in probabilities be between outputs if there were only 2^8 input values and 100 output values? What about if there were 2^8 input values and 128 output values? 4. Tricky: How would you design a pseudorandom number generator over arbitrary output ranges where all of the output values are exactly equally likely?
Views: 5621 Jorge Rodriguez
Linear Congruential Random Number Generators
 
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Random Number Generators (RNGs) are useful in many ways. This video explains how a simple RNG can be made of the 'Linear Congruential Generator' type. This type of generator is not very robust, but it is quick and easy to program with little memory requirement.
Views: 19269 physics qub
Openwest 2015 - Robert Stone - "Pseudo-Random Number Generation" (91)
 
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As advanced as computers have become they are still deterministic creatures at heart. With revelations by Edward Snowden surrounding Ellipitic Curve Cryptography and the discovery that the NSA and CIA were involved in the development of one of the RSA's psuedo-random number generators questions abound as to "What do those three letter agencies actually know and what can they do with this information?" This presentation introduces the concept of Psuedo and Truly Random number generation, provides an overview of the different types of algorithms used in their generation, and then dives into a discussion about the Math and Theory behind how Prime Numbers and Elliptic Curves factor into the generation of psuedo-random numbers. An analysis of Dual_EC_DRBG is presented making it clear what the problem actually was and just how naughty the government has been! Best practices and gotchas are also outlined, a discussion regarding where randomness comes from in Perl as well as a few case studies are presented so that developers can protect themselves from common mistakes. A background in Perl is not required and you are sure to find this presentation fun, entertaining, and just a bit random! Friday, May 8th, 10:30am-11:15am Room SB 073 (Security)
Views: 407 Utah Open Source
Applied Cryptography: Random Numbers in Java (1/5)
 
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Previous video: https://youtu.be/KuthrX4G1ss Next video: https://youtu.be/FhrsUCICh-Y
Views: 977 Leandro Junes
6.875 (Cryptography) L7: Pseudorandom Functions
 
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MIT's Spring 2018 Cryptography & Cryptanalysis Class (6.875) Prof. Vinod Vaikuntanathan
Views: 99 Andrew Xia
Truly Random Number Generator Is Bringing Encryption To Every Device
 
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So long pseudo-random numbers. Quantum mechanics is making encryption much stronger.
Views: 46 Sara Peters
GATE 2015 ECE  Contents of Pseudo Random Number Generator after three clock cycles
 
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for a D flip flop, Next state is same as input D but with one clock delay, thats why D flip flop is called as Delay flip flop
Views: 8258 GATE paper
Prng Implementation - Applied Cryptography
 
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This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 3176 Udacity
Applied Cryptography: Random Numbers in Java (5/5)
 
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Previous video: https://youtu.be/KnHp1uSm6k0 Next video: https://youtu.be/8VlG5lq4xLs
Views: 379 Leandro Junes
Introduction to Random Numbers in Security (CSS322, L8, Y14)
 
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Short introduction to challenges of generating random numbers for cryptography. Course material via: http://sandilands.info/sgordon/teaching
Views: 373 Steven Gordon
Pseudo Random Number Generator
 
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Pseudo Random Number Generator
Views: 17 Luke Arnull
John von Neumann's First Pseudorandom Number Generator
 
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http://demonstrations.wolfram.com/JohnVonNeumannsFirstPseudorandomNumberGenerator The Wolfram Demonstrations Project contains thousands of free interactive visualizations, with new entries added daily. Pseudorandom number generators have applications in many areas: simulation, game-playing, cryptography, statistical sampling, evaluation of multiple integrals, and computations in statistical physics, to name a few. The method illustrated in this Demons... Contributed by: Hector Zenil
Views: 1213 wolframmathematica
NMCS4ALL: Random number generators
 
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Twenty minute introduction to randomness and pseudorandom number generators, with demos. The New Mexico CS for All project is teaching computational thinking and programming. Production supported by the National Science Foundation, award # CNS 1240992
Views: 26646 Dave Ackley

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