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
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Views: 162806
Khan Academy Labs

Pseudo random number generators; stream ciphers. Course material via: http://sandilands.info/sgordon/teaching

Views: 2501
Steven Gordon

Views: 12482
Eddie Woo

Cryptography
To get certificate subscribe: https://www.coursera.org/learn/cryptography
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https://www.facebook.com/cyberassociation/

Views: 2697
intrigano

This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.

Views: 9303
Udacity

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: 22337
Steven Gordon

For slides, a problem set and more on learning cryptography, visit www.crypto-textbook.com

Views: 103633
Introduction to Cryptography by Christof Paar

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
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Previous Episode
How many Cops to catch a Robber? | Infinite Series
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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
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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: 126444
PBS Infinite Series

Cryptography
To get certificate subscribe: https://www.coursera.org/learn/cryptography
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https://www.facebook.com/cyberassociation/

Views: 3823
intrigano

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: 28569
Coding Math

This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.

Views: 3651
Udacity

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: 4954
Raj Jain

I do an example of finding pseudorandom numbers.

Views: 1201
Michael Venn

An introduction to linear feedback shift registers, and their use in generating pseudorandom numbers for Vernam ciphers.
For more cryptography, subscribe to my channel: https://www.youtube.com/channel/UC1KV5WfubHTV6E7sVCnTidw

Views: 33659
Jeff Suzuki

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: 3477
The Audiopedia

Learn how to Create Random Values using Crypto Module in NodeJS.

Views: 1885
DevNami

Views: 7750
Internetwork Security

Talk at crypto 2012. Authors: Eric Miles, Emanuele Viola. See http://www.iacr.org/cryptodb/data/paper.php?pubkey=24289

Views: 7585
TheIACR

This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.

Views: 1433
Udacity

MIT's Spring 2018 Cryptography & Cryptanalysis Class (6.875)
Prof. Vinod Vaikuntanathan

Views: 268
Andrew Xia

Part 1 of a 3 part lesson on Pseudo Random Number Generators (PRNGs)

Views: 982
Mobile Computer Science Principles

Previous video: https://youtu.be/KuthrX4G1ss
Next video: https://youtu.be/FhrsUCICh-Y

Views: 1046
Leandro Junes

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: 692
intrigano

Views: 225
Decision modeling

Cryptographically secure pseudorandom number generator Top # 7 Facts

Views: 93
Duryodhan Trivedi

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: 3066
White Hat Cal Poly

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: 2965
KOLT KU

PRNGs with block ciphers in counter and OFB mode; ANSI X9.17; RC4. Course material via: http://sandilands.info/sgordon/teaching

Views: 1417
Steven Gordon

Fundamental concepts of Pseudorandom Number Generation are discussed. Pseudorandom Number Generation using a Block Cipher is explained. Stream Cipher & RC4 are presented.

Views: 1286
Scholartica Channel

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: 6163
Coding Math

http://www.atozsky.com/
https://www.facebook.com/atozsky.computer/
All credits goes to NIELIT, Delhi INDIA

Views: 518
AtoZ COMPUTER

Previous video: https://youtu.be/g3iH74XFaT0
Next video:

Views: 1509
Leandro Junes

True and pseudo random numbers; Linear Congruential Generator. Course material via: http://sandilands.info/sgordon/teaching

Views: 3443
Steven Gordon

Part 1 of the course: https://youtu.be/GGILQcO843s
Part 2 of the course: https://youtu.be/4RnqrLeY4xY
Book: Understanding Cryptography
https://www.amazon.com/Understanding-Cryptography-Textbook-Students-Practitioners/dp/3642041000/ref=as_li_ss_tl?ie=UTF8&qid=1541146284&sr=8-1&keywords=Understanding+Cryptography:+A+Textbook+for+Students+and+Practitioners&linkCode=sl1&tag=julianhosp-20&linkId=8e14aad9056003d3eefcacb57c2e0b73&language=en_US
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New to cryptocurrencies? You might want to read this book first!
http://cryptofit.community/cryptobook
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My name is Dr. Julian Hosp or just Julian.
My videos are about Bitcoin, Ethereum, Blockchain and crypto currencies in general, to avoid scam, rip-off and fraud especially in mining. I'm talking about how you can invest wisely and do it rationally and simply. My ultimate goal is to make people all around the world #CRYPTOFIT. I.E fit for this new wave of decentralization and blockchain. Have fun!
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Views: 701
Dr. Julian Hosp

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."
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Views: 190422
Seeker

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: 40277
Art of the Problem

Raghu Meka, UCLA
https://simons.berkeley.edu/talks/pseudorandom-generators-1
Pseudorandomness Boot Camp

Views: 1114
Simons Institute

Spring 2018 Cryptography & Cryptanalysis
Prof. Vinod Vaikuntanathan

Views: 261
Andrew Xia

How Software Works is a book and video series explaining the magic behind software encryption, CGI, video game graphics, and a lot more. The book uses plain language and lots of diagrams, so no technical or programming background is required. Come discover what's really happening inside your computer!
This episode is about random numbers--why software needs them, why they can't really make them, and why that's okay.
Learn more about the book...
- At the Amazon page (http://amzn.to/1mZ276M).
- At my publisher (http://www.nostarch.com/howsoftwareworks)
- At my site (http://www.vantonspraul.com/HSW).
If you'd like to contact me visit my site (http://vantonspraul.com), or just leave a comment below. Suggestions for future topics are welcome!

Views: 17081
V. Anton Spraul

www.facebook.com/mrcrucialclothing
- visit and post for chances to win free art&apparel
How to Build a 16 Bit Fibonacci LFSR in Minecraft.
THERE IS a complete Tutorial on how to Build the Shift Register we are working from. This Ciruit Generates pseudorandom numbers in minecraft. We Start out with a 16 BIT Serail Out Shift Register, Shifting Right to Left.
The most commonly used linear function of single bits is XOR. Thus, an LFSR is most often a shift register whose input bit is driven by the exclusive-or (XOR) of some bits of the overall shift register value.
The initial value of the LFSR is called the seed, and because the operation of the register is deterministic, the stream of values produced by the register is completely determined by its current (or previous) state. Likewise, because the register has a finite number of possible states, it must eventually enter a repeating cycle. However, an LFSR with a well-chosen feedback function can produce a sequence of bits which appears random and which has a very long cycle.
The 16 Bit Register:
This Shift Register is a cascade of 1 Wide D Flip-Flops, sharing the same clock, which has the output of any one but the last flip-flop connected to the "data" input of the next one in the chain, resulting in a circuit that shifts by one position, when enabled to do so by a transition of the clock input. Video Tutorial link below..
Fibonacci LFSR:
The bit positions that affect the next state are called the TAPS. [16,14,13,11]. The rightmost bit of the LFSR is called the output bit. The taps are XOR'd sequentially with the output bit and then fed back into the leftmost bit. The sequence of bits in the rightmost position is called the output stream.
The sequence of numbers generated by an LFSR can be considered a binary numeral system just as valid as Gray code or the natural binary code.
The arrangement of taps for feedback in an LFSR can be expressed in finite field arithmetic as a polynomial mod 2. This means that the coefficients of the polynomial must be 1's or 0's. This is called the feedback polynomial or characteristic polynomial.
Pseudorandomness:
A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers that approximates the properties of random numbers. The sequence is not truly random in that it is completely determined by a relatively small set of initial values, called the PRNG's state, which includes a truly random seed. pseudorandom numbers are important in practice for their speed in number generation and their reproducibility, and they are thus central in applications such as simulations, in cryptography, and in procedural generation. Good statistical properties are a central requirement for the output of a PRNG, and common classes of suitable algorithms include linear congruential generators, lagged Fibonacci generators, and linear feedback shift registers.
Applications:
generating pseudo-random numbers, pseudo-noise sequences, fast digital counters, and whitening sequences, Rave House.
Shift Register Tutorial:
http://www.youtube.com/watch?v=LgAZ5iRsrLM
Linear Feedback Shift Register:
http://en.wikipedia.org/wiki/Linear_feedback_shift_register
Pseudorandomness:
http://en.wikipedia.org/wiki/Pseudorandom_number_generator

Views: 20563
MRCRUCIAL

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: 25035
physics qub

I should have said p = 3 (mod 4) and q = 3 (mod 4)
https://asecuritysite.com/encryption/blum

Views: 184
Bill Buchanan OBE

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: 5799
Jorge Rodriguez

This project presents a quantum random number generator for a multitude of cryptographic applications based on the alpha decay of a household radioactive source.

Views: 688
BTYoungScientists

This is another video in my series of videos where I talk about Digital Logic. In this video, I show how you can make a Linear Feedback Shift Register, which is a circuit that allows you to generate pseudo-random numbers.

Views: 41900
Robot Brigade

Medium: https://medium.com/asecuritysite-when-bob-met-alice/for-control-of-the-internet-its-bots-v-humans-solving-captchas-in-a-crediable-and-secret-way-d947c21ec62a?source=friends_link&sk=871062acea1a89d5c26f538cf6744011
Coding: https://asecuritysite.com/encryption/vop

Views: 111
Bill Buchanan OBE