Search results “Pseudo random numbers cryptography”

Random vs. Pseudorandom Number Generators
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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).
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Views: 157170
Khan Academy Labs

Cryptography
To get certificate subscribe: https://www.coursera.org/learn/cryptography
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Views: 1923
intrigano

Previous video: https://youtu.be/6ro3z2pTiqI
Next video: https://youtu.be/KuthrX4G1ss

Views: 4039
Leandro Junes

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; 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

Views: 10437
Eddie Woo

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

Views: 2732
intrigano

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

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

Views: 2228
Steven Gordon

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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Seeker

Views: 5143
Internetwork Security

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

I do an example of finding pseudorandom numbers.

Views: 572
Michael Venn

Views: 26083
Jeff Suzuki

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
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

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

Views: 96456
Introduction to Cryptography by Christof Paar

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

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

Views: 930
Simons Institute

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

Views: 2642
Udacity

Spring 2018 Cryptography & Cryptanalysis
Prof. Vinod Vaikuntanathan

Views: 147
Andrew Xia

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

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

Views: 1344
Leandro Junes

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

Views: 633
Mobile Computer Science Principles

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

Views: 3152
Steven Gordon

*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.

Views: 8110
Programming Electronics Academy

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
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

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

Views: 82
Duryodhan Trivedi

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

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

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

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

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

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

Views: 977
Leandro Junes

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

Views: 99
Andrew Xia

So long pseudo-random numbers. Quantum mechanics is making encryption much stronger.

Views: 46
Sara Peters

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

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

Views: 3176
Udacity

Previous video: https://youtu.be/KnHp1uSm6k0
Next video: https://youtu.be/8VlG5lq4xLs

Views: 379
Leandro Junes

Short introduction to challenges of generating random numbers for cryptography. Course material via: http://sandilands.info/sgordon/teaching

Views: 373
Steven Gordon

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

Views: 164
Decision modeling

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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© 2019 Public finance in theory and practice musgrave

Bring Your Own Encryption. Learn about customer-managed encryption, and why businesses should stay in control of their encrypted content in the cloud. Securing Business Information in the Cloud. Explore how a new generation of secure, enterprise cloud services mitigates security risks by centralizing documents in one platform. Design Thinking and Enterprise Security. How to Protect Content in the Age of Distributed Computing. Adapting security controls to protect sensitive content has proven difficult in the mobile workplace. Learn how you can secure your content and prevent data loss. Bridging The Cloud Encryption Gap. Learn how you can bridge the cloud encryption gap with customer-managed encryption keys. 10 Lessons from Tech Leaders on Digital Transformation. 4 Ways to Build Better Apps with Secure Content Services. 5 Counterintuitive Mistakes Made by Companies Going Digital. Learn how to make the right decisions upfront while building your digital business. Whitepapers. Explore the four key points you should consider when deciding between cloud versus hybrid for your business. The Future of Security.