Home
Search results “Pseudorandom number generation in cryptography math”

13:59
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: 22046 Coding Math

06:41
Views: 154392 Khan Academy Labs

14:04
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: 5248 Coding Math

14:19
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: 99676 PBS Infinite Series

14:02
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: 5532 Jorge Rodriguez

01:42
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 7710 Udacity

42:52
Pseudo random number generators; stream ciphers. Course material via: http://sandilands.info/sgordon/teaching
Views: 2097 Steven Gordon

19:39
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: 2383 intrigano

40:42
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: 4374 Raj Jain

11:37
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: 1659 intrigano

35:59
Fundamental concepts of Pseudorandom Number Generation are discussed. Pseudorandom Number Generation using a Block Cipher is explained. Stream Cipher & RC4 are presented.
Views: 1207 Scholartica Channel

13:30
Views: 9513 Eddie Woo

16:31
Previous video: https://youtu.be/6ro3z2pTiqI Next video: https://youtu.be/KuthrX4G1ss
Views: 3571 Leandro Junes

01:05:35
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: 20607 Steven Gordon

03:13
Part 1 of a 3 part lesson on Pseudo Random Number Generators (PRNGs)

09:47
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: 2517 KOLT KU

57:57
True and pseudo random numbers; Linear Congruential Generator. Course material via: http://sandilands.info/sgordon/teaching
Views: 3016 Steven Gordon

05:34
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: 2542 The Audiopedia

00:57
Cryptographically secure pseudorandom number generator Top # 7 Facts
Views: 75 Duryodhan Trivedi

02:50

28:24
Views: 4526 Internetwork Security

50:14
http://www.atozsky.com/ https://www.facebook.com/atozsky.computer/ All credits goes to NIELIT, Delhi INDIA
Views: 191 AtoZ COMPUTER

01:14
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 2971 Udacity

01:20
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 2571 Udacity

19:12
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: 2952 White Hat Cal Poly

28:12
Previous video: https://youtu.be/g3iH74XFaT0 Next video:
Views: 1252 Leandro Junes

50:07
PRNGs with block ciphers in counter and OFB mode; ANSI X9.17; RC4. Course material via: http://sandilands.info/sgordon/teaching
Views: 1028 Steven Gordon

04:31
12 Methods: Pseudo Random Numbers Exercises
Views: 31 Merici Maths

33:51

03:44
Part 2 of a 3 part lesson on PRNGs.

01:04:17
Raghu Meka, UCLA https://simons.berkeley.edu/talks/pseudorandom-generators-1 Pseudorandomness Boot Camp
Views: 822 Simons Institute

02:36
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 1318 Udacity

05:18
I do an example of finding pseudorandom numbers.
Views: 307 Michael Venn

19:48
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: 349 intrigano

20:35
Jean Paul Degabriele and Kenneth G. Paterson and Jacob C. N. Schuldt and Joanne Woodage, Crypto 2016. See http://www.iacr.org/cryptodb/data/paper.php?pubkey=27651
Views: 319 TheIACR

04:49
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: 182036 Seeker

01:29
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: 37923 Art of the Problem

19:48
Lectures on Introduction to Cryptography.
Views: 54 Wobbly Bit

10:14
Views: 22699 Jeff Suzuki

50:43
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: 406 Utah Open Source

13:33
Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: https://to.pbs.org/donateinfi Symmetric keys are essential to encrypting messages. How can two people share the same key without someone else getting a hold of it? Upfront asymmetric encryption is one way, but another is Diffie-Hellman key exchange. This is part 3 in our Cryptography 101 series. Check out the playlist here for parts 1 & 2: https://www.youtube.com/watch?v=NOs34_-eREk&list=PLa6IE8XPP_gmVt-Q4ldHi56mYsBuOg2Qw Tweet at us! @pbsinfinite Facebook: facebook.com/pbsinfinite series Email us! pbsinfiniteseries [at] gmail [dot] com Previous Episode Topology vs. “a” Topology https://www.youtube.com/watch?v=tdOaMOcxY7U&t=13s Symmetric single-key encryption schemes have become the workhorses of secure communication for a good reason. They’re fast and practically bulletproof… once two parties like Alice and Bob have a single shared key in hand. And that’s the challenge -- they can’t use symmetric key encryption to share the original symmetric key, so how do they get started? Written and Hosted by Gabe Perez-Giz Produced by Rusty Ward Graphics by Ray Lux Assistant Editing and Sound Design by Mike Petrow and Meah Denee Barrington Made by Kornhaber Brown (www.kornhaberbrown.com) Thanks to Matthew O'Connor, Yana Chernobilsky, and John Hoffman who are supporting us on Patreon at the Identity level! And thanks to Nicholas Rose, Jason Hise, Thomas Scheer, Marting Sergio H. Faester, CSS, and Mauricio Pacheco who are supporting us at the Lemma level!
Views: 46302 PBS Infinite Series

01:26
Views: 73 Rishika Janaki

01:13:12
Raghu Meka, UCLA https://simons.berkeley.edu/talks/pseudorandom-generators-IV Pseudorandomness Boot Camp
Views: 221 Simons Institute

00:47
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 766 Udacity

19:48
Views: 678 Osiris Salazar

01:03

20:10
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: 25737 Dave Ackley

04:26
Crypto 2011 Rump session presentation for Abhishek Banerjee, Chris Peikert, Alon Rosen, talk given by Chris Peikert
Views: 625 TheIACR

30:26
Views: 141 Decision modeling

55:31
Raghu Meka, UCLA https://simons.berkeley.edu/talks/pseudorandom-generators-II Pseudorandomness Boot Camp
Views: 222 Simons Institute

Special education assistant resume cover letter
Job cover letter salutation line
Legal investigator cover letter
Diversity officer cover letter
Vthumb application letters