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Search results “Pseudo random generators cryptography”

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Raghu Meka, UCLA https://simons.berkeley.edu/talks/pseudorandom-generators-1 Pseudorandomness Boot Camp
Views: 1114 Simons Institute

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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: 692 intrigano

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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: 2965 KOLT KU

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Views: 12469 Eddie Woo

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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: 3640 Udacity

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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: 9297 Udacity

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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: 28548 Coding Math

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Previous video: https://youtu.be/6ro3z2pTiqI Next video: https://youtu.be/KuthrX4G1ss
Views: 4696 Leandro Junes

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Raghu Meka, UCLA https://simons.berkeley.edu/talks/pseudorandom-generators-IV Pseudorandomness Boot Camp
Views: 231 Simons Institute

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MIT's Spring 2018 Cryptography & Cryptanalysis Class (6.875) Prof. Vinod Vaikuntanathan
Views: 268 Andrew Xia

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Raghu Meka, UCLA https://simons.berkeley.edu/talks/pseudorandom-generators-II Pseudorandomness Boot Camp
Views: 238 Simons Institute

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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: 3064 White Hat Cal Poly

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Views: 692 VideosCoursera

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Pseudo random number generators; stream ciphers. Course material via: http://sandilands.info/sgordon/teaching
Views: 2501 Steven Gordon

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Spring 2018 Cryptography & Cryptanalysis Prof. Vinod Vaikuntanathan
Views: 261 Andrew Xia

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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: 126400 PBS Infinite Series

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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: 22329 Steven Gordon

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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: 1433 Udacity

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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: 4953 Raj Jain

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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: 6159 Coding Math

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Views: 415 Victor

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Views: 802 Osiris Salazar

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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: 3474 The Audiopedia

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

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Views: 7743 Internetwork Security

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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: 2787 Udacity

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Pseudorandom generators (definitions and constructions; the hybrid method), a lecture by Benny Applebaum. The topic of the 4th Annual Bar-Ilan Winter School on Cryptography held in January 2014, was Symmetric Encryption in Theory and in Practice. The winter school studied symmetric encryption in theory and in practice, and included a study of the theoretical foundations of symmetric encryption on the one hand, and practical constructions and cryptanalysis on the other hand. As every year, the event organizers were Prof. Yehuda Lindell and Prof. Benny Pinkas, of BIU's Department of Computer Science. This year,the Winter School featured speakers from such institutions as the Royal Holloway at the University of London , and the University of Wisconsin - Madison. For all videos of this playlist: https://www.youtube.com/playlist?list=PLXF_IJaFk-9BmvxWhnxPId32CPJhVtU6D 4th Annual Bar-Ilan Winter School on Cryptography: http://crypto.biu.ac.il/winterschool2014/ Prof. Lindell's Lab http://www1.biu.ac.il/indexE.php?id=8043&pt=30&pid=7711&level=2&cPath=7702,7711,8043 Prof. Pinkas' Lab http://www1.biu.ac.il/indexE.php?id=8046&pt=30&cPath=7702,7711,8046 Dept. of Computer Science: http://cs.biu.ac.il/en/ Bar-Ilan University: http://www1.biu.ac.il/en
Views: 713 barilanuniversity

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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: 33603 Jeff Suzuki

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True and pseudo random numbers; Linear Congruential Generator. Course material via: http://sandilands.info/sgordon/teaching
Views: 3441 Steven Gordon

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

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Views: 19 DHANRAJ V

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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: 1286 Scholartica Channel

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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: 24990 physics qub

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Views: 699 Dr. Julian Hosp

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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: 28123 Dave Ackley

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Lectures on Introduction to Cryptography.
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Cryptographically secure pseudorandom number generator Top # 7 Facts
Views: 93 Duryodhan Trivedi

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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

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Views: 225 Decision modeling

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Previous video: https://youtu.be/g3iH74XFaT0 Next video:
Views: 1509 Leandro Junes

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In cryptography, a zero-knowledge proof or zero-knowledge protocol is a method by which one party (the prover) can prove to another party (the verifier) that they know a value x, without conveying any information apart from the fact that they know the value x. The essence of zero-knowledge proofs is that it is trivial to prove that one possesses knowledge of certain information by simply revealing it; the challenge is to prove such possession without revealing the information itself or any additional information. If proving a statement requires that the prover possess some secret information, then the verifier will not be able to prove the statement to anyone else without possessing the secret information. The statement being proved must include the assertion that the prover has such knowledge, but not the knowledge itself. Otherwise, the statement would not be proved in zero-knowledge because it provides the verifier with additional information about the statement by the end of the protocol. A zero-knowledge proof of knowledge is a special case when the statement consists only of the fact that the prover possesses the secret information. Interactive zero-knowledge proofs require interaction between the individual (or computer system) proving their knowledge and the individual validating the proof. A protocol implementing zero-knowledge proofs of knowledge must necessarily require interactive input from the verifier. This interactive input is usually in the form of one or more challenges such that the responses from the prover will convince the verifier if and only if the statement is true, i.e., if the prover does possess the claimed knowledge. If this were not the case, the verifier could record the execution of the protocol and replay it to convince someone else that they possess the secret information. The new party's acceptance is either justified since the replayer does possess the information (which implies that the protocol leaked information, and thus, is not proved in zero-knowledge), or the acceptance is spurious, i.e., was accepted from someone who does not actually possess the information. Some forms of non-interactive zero-knowledge proofs exist, but the validity of the proof relies on computational assumptions (typically the assumptions of an ideal cryptographic hash function). Lecture 1 Encryption Schemes Lecture 2 Probabilistic and Game based Security Definitions Lecture 3 Reduction Proofs - What are they? Lecture 4 Reduction Proofs - How to do? Lecture 5 Pseudo Random Generators Lecture 6 Reduction Proof Example - PRG based Encryption Lecture 7 Reduction Proof Examples - PRF Family Lecture 8 PRG Output Expansion Lecture 9 Hybrid Proofs - Defining Hybrids Lecture 10 Hybrid Proof Example - PRG Output Expansion Lecture 11 Random Oracle Model ROM Lecture 12 ROM Construction Example - CPA secure RSA Lecture 13 ROM Proof Example - CPA secure RSA Lecture 14 ROM Construction Examples - RSA FDH Signatures Lecture 15 ROM Proof Examples - RSA FDH Signatures For more topics please check the link bellow: https://www.youtube.com/playlist?list=PLOBV8lhF_YPtE-P5D8mJZVqy3p4w2kWvp https://www.youtube.com/playlist?list=PLOBV8lhF_YPtujh1D7kzZw1Z2vve5huuZ
Views: 9 Vijay S

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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

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Views: 111 Bill Buchanan OBE

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