Search results “Pseudo random generators cryptography”

Cryptography
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https://www.facebook.com/cyberassociation/

Views: 1648
intrigano

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.
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Views: 154293
Khan Academy Labs

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

Views: 7693
Udacity

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

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

Views: 3558
Leandro Junes

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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
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: 99593
PBS Infinite Series

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: 22005
Coding Math

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: 20589
Steven Gordon

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: 345
intrigano

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: 2373
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: 4366
Raj Jain

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

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

Views: 2096
Steven Gordon

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

Views: 819
Simons Institute

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

Views: 1206
Scholartica Channel

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: 7508
GATE paper

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

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: 2533
The Audiopedia

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

Views: 2565
Udacity

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: 685
barilanuniversity

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

Views: 3012
Steven Gordon

Views: 4512
Internetwork Security

Lectures on Introduction to Cryptography.

Views: 54
Wobbly Bit

Spring 2018 Cryptography & Cryptanalysis
Prof. Vinod Vaikuntanathan

Views: 84
Andrew Xia

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: 5531
Jorge Rodriguez

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

Views: 190
AtoZ COMPUTER

The construction is based on sponge functions and suitable for embedded security devices as it requires few resources. What is pseudo random number generator (prng)? Definition vspseudorandom from wolfram mathworldwhat pseudorandom generator? does and numbers lixpseudo generators. Let g be a generator that, given seed input s, outputs (longer) string g(s). Pseudorandom number generator wikipedia. Sok security models for pseudo random number generators. More recently, the mixmax prng has been included in root and class library for high energy physics (clhep) software packages claims to be a state of art generator due its long period, List random number generators wikipedia. A computer follows its instructions blindly and is therefore completely predictable. The prefix pseudo is used to distinguish this type of number from a 'truly' random generated by physical process such as radioactive decay. It is required in fundamental tasks such as key 3 jul 2017 abstract the pseudo random number generators (prngs) are tools monte carlo simulations. Consider also a polynomial time algorithm that is given access to oracle will either output g(s) for some unknown seed s or sequence r of the same length pseudo random number generators. We propose a model for such generators and explain how to define one on top of sponge function cryptanalytic attacks pseudorandombruce schneier abstract. Statistical tests for mixmax pseudorandom number generator. Many applications don't have source of truly random bits; Instead they use prngs to generate these numbers. There are two main approaches to generating random numbers using a computer pseudo number generators (prngs) and true generator (prng) is program written for, used in, probability statistics applications when large quantities of digits needed generator(prng) refers an algorithm that uses mathematical formulas produce sequences. C and binary code libraries for generating floating point integer random numbers with uniform non distributions. Pseudorandom number generators (video) random introduction to randomness and numbers. What is pseudo random number generator (prng)? Definition (prng) geeksforgeekswhat slideshare. Pseudorandom number generator wikipedia
a pseudorandom (prng), also known as deterministic random bit (drbg), is an algorithm for generating sequence of numbers whose properties approximate the sequences. Frrandomness plays an important role in multiple applications cryptog raphy. In this paper we discuss prngs the mechanisms used by real world secure systems to generate cryptographic keys, initialization vectors, random nonces, and other values sok security models for pseudo randomoppida, 6 avenue du vieil etang, 78180 montigny le bretonneux, france sylvain. Pseudo random a pseudo number generator (prng) refers to an algorithm that uses mathematical formulas produce sequences of numbers. See also quasirandom sequence, random number29 apr 2017introduction to pseudorandom numberssome number generator

Views: 94
Roselyn Wnuk Tipz

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

Views: 219
Simons Institute

Cryptographically secure pseudorandom number generator Top # 7 Facts

Views: 74
Duryodhan Trivedi

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

Views: 2971
Udacity

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

Views: 221
Simons Institute

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: 181992
Seeker

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

Views: 1252
Leandro Junes

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: 36382
Robot Brigade

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: 37894
Art of the Problem

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

Pseudorandom functions and permutations (definitions and constructions) , a lecture by Iftach Haitner.
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: 1248
barilanuniversity

One-photon based quantum technologies
In this lesson, you will discover two quantum technologies based on one photon sources. Quantum technologies allow one to achieve a goal in a way qualitatively different from a classical technology aiming at the same goal. For instance, quantum cryptography is immune to progress in computers power, while many classical cryptography methods can in principle be broken when we have more powerful computers. Similarly, quantum random number generators yield true random numbers, while classical random number generators only produce pseudo-random numbers, which might be guessed by somebody else than the user. This lesson is also an opportunity to learn two important concepts in quantum information: (i) qubits based on photon polarization; (ii) the celebrated no-cloning theorem, at the root of the security of quantum cryptography.
Learning Objectives
• Apply your knowledge about the behavior of a single photon on a beam splitter to quantum random number generators.
• Understand the no-cloning theorem
• Understand and remember the properties of q qubit
This course gives you access to basic tools and concepts to understand research articles and books on modern quantum optics. You will learn about quantization of light, formalism to describe quantum states of light without any classical analogue, and observables allowing one to demonstrate typical quantum properties of these states. These tools will be applied to the emblematic case of a one-photon wave packet, which behaves both as a particle and a wave. Wave-particle duality is a great quantum mystery in the words of Richard Feynman. You will be able to fully appreciate real experiments demonstrating wave-particle duality for a single photon, and applications to quantum technologies based on single photon sources, which are now commercially available. The tools presented in this course will be widely used in our second quantum optics course, which will present more advanced topics such as entanglement, interaction of quantized light with matter, squeezed light, etc... So if you have a good knowledge in basic quantum mechanics and classical electromagnetism, but always wanted to know: • how to go from classical electromagnetism to quantized radiation, • how the concept of photon emerges, • how a unified formalism is able to describe apparently contradictory behaviors observed in quantum optics labs, • how creative physicists and engineers have invented totally new technologies based on quantum properties of light, then this course is for you.
Subscribe at: https://www.coursera.org

Views: 186
intrigano

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

This chapter introduces why random shifts result in perfect secrecy. We explore hardware random number generators vs. pseudorandom number generators which expand a short random seed into a long sequences of "random looking" numbers. If used in cryptography, can offer "practical security" which is based on the secrecy of the random seed only. Alice and Bob can safely assume that the "enemy knows the system" (one-time pad + pseudorandom generator), and focus on assuring their seed is shared in secret & generated randomly.
If you are still wondering: how exactly would Eve break a ciphertext encrypted with a pseudorandom list of shifts? Remember: the pseudorandom generator can only produce a tiny (0.0000...1%) fraction of all possible shift sequences. Eve could program a computer to generate all possible sequences (starting from seed 0, 1, 2, 3.....) and reverse all of these shifts. When the computer hits a seed which decrypts the ciphertext into an "English looking" sentence, she can be 99.999% sure it is the message. All of the other seeds will decrypt into unreadable (jumbled looking) messages.

Views: 28358
Art of the Problem

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: 20292
MRCRUCIAL

Views: 22612
Jeff Suzuki

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: 604
BTYoungScientists

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