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Search results “Cryptographically secure random generator”

16:31
Previous video: https://youtu.be/6ro3z2pTiqI Next video: https://youtu.be/KuthrX4G1ss
Views: 3054 Leandro Junes

01:42
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 7105 Udacity

06:41
Views: 150283 Khan Academy Labs

13:30
Views: 8544 Eddie Woo

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: 1283 intrigano

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: 2092 The Audiopedia

00:57
Cryptographically secure pseudorandom number generator Top # 7 Facts
Views: 64 Duryodhan Trivedi

14:53
Speaker: Sean Boyce Security Researcher The problem of generating "reasonable" approximations to random numbers has been solved quite some time ago... but this talk is not for reasonable people. Generating true random numbers with a deterministic system is impossible; and so we must drink deeply from the raw, godless chaos of quantum physics. This talk will cover the various pitfalls of quantum true random number generator construction, including bias, statistical relatedness between bits, and unpleasant supply voltages. A working reference design that overcomes these hurdles will be described, and barring major disaster, demonstrated. Notably, this design contains a custom, fully solid-state particle detector that may be constructed for around USD 20\$. To benefit the most from this lecture, a very basic knowledge of statistics, particle physics, and/or analog electronics is ideal; however enough background will be provided that this will not be strictly necessary. If in doubt, the Wikipedia articles on quantum tunneling, alpha particle, normal distribution, operational amplifier, and hardware random number generator should provide more than sufficient background. Demo For more information visit: http://bit.ly/defcon17_information To download the video visit: http://bit.ly/defcon17_videos
Views: 4910 Christiaan008

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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: 12174 Anders Brownworth

12:48
Previous video: https://youtu.be/KuthrX4G1ss Next video: https://youtu.be/FhrsUCICh-Y
Views: 850 Leandro Junes

08:27
How Software Works is a book and video series explaining the magic behind software encryption, CGI, video game graphics, and a lot more. The book uses plain language and lots of diagrams, so no technical or programming background is required. Come discover what's really happening inside your computer! This episode is about random numbers--why software needs them, why they can't really make them, and why that's okay. Learn more about the book... - At the Amazon page (http://amzn.to/1mZ276M). - At my publisher (http://www.nostarch.com/howsoftwareworks) - At my site (http://www.vantonspraul.com/HSW). If you'd like to contact me visit my site (http://vantonspraul.com), or just leave a comment below. Suggestions for future topics are welcome!
Views: 7735 V. Anton Spraul

09:23
Views: 295480 Numberphile

10:38
Previous video: https://youtu.be/KnHp1uSm6k0 Next video: https://youtu.be/8VlG5lq4xLs
Views: 329 Leandro Junes

02:09
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: 563 BTYoungScientists

21:05
Previous video: https://youtu.be/_IcG4N7PQfA Next video: https://youtu.be/uTlZHRa-ZkM
Views: 515 Leandro Junes

28:12
Previous video: https://youtu.be/g3iH74XFaT0 Next video:
Views: 1088 Leandro Junes

15:24
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: 589 WikiAudio

01:14
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 2722 Udacity

01:26
Views: 62 Rishika Janaki

53:06
Randomness is essential to cryptography: cryptographic security depends on private keys that are unpredictable to an attacker. But how good are the random number generators that are actually used in practice? In this talk, I will discuss several large-scale surveys of cryptographic deployments, including TLS, SSH, Bitcoin, and smart cards, and show that random number generation flaws are surprisingly widespread. We will see how many of the most commonly used public key encryption and signature schemes, including RSA, DSA, and ECDSA are brittle if used with faulty random number generators and can fail catastrophically to an external attacker. We trace many of the the random number generation flaws we encountered to specific implementations and vulnerable implementation patterns. I will also discuss followup work showing that, distressingly, many hosts with random number generation flaws remain unpatched years after public disclosure. This talk surveys several joint projects with a very large number of collaborators.
Views: 297 ECRYPT

17:02
Previous video: https://youtu.be/uTlZHRa-ZkM Next video: https://youtu.be/KCcJE8l__H0
Views: 423 Leandro Junes

01:29:58
Random number generation underlies all of cryptography—if you can’t generate good random numbers, you probably can’t do any useful crypto. In this tutorial, I will go over how cryptographic random number generation works, and then zoom in on entropy sources—the ultimate source of unpredictability in any cryptographic RNG. I’ll discuss the problems of designing and analyzing an entropy source, and the approach we’ve used in SP 800-90B for specifying how they should work and how labs should try to validate them. I’ll also talk about the related problem of extractors, the functions that process entropy-bearing inputs and yield some kind of seed for a deterministic RNG.

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In this video Luka Matic explains why and how he designed and built a super duper random number generator that passes official regulatory (DIN, NIST, etc.) tests for randomness. Based on noise Zener diodes the circuit fills an SD card with files of up to 4 GB of really random data. On http://www.elektormagazine.com Luka writes: This Random Number Generator uses Zener diodes to generate avalanche noise signal, then differential amplifier (and a few analog filters) to eliminate deterministic effects. The noise signal is captured by ATTiny2313 and sent to an FAT32 SD card as a sequence of meaningless hex numbers. I tested random number sequences in MATLAB to check for randomness. I designed and produced a prototype PCB (that you can see on the elektormagazine website (https://www.elektormagazine.com/labs/random-number-generator-150116). This RNG is designed with cheap and ubiquitous components and still creates random sequences of good randomness. I tried all the methods that I know to analyze the random sequences. If you know more about mathematical methods for checking the randomness of the sequence, you could be interested to check yourself. I have the circuit schematics drawn on paper.
Views: 3885 www.elektor.tv

09:32
Previous video: https://youtu.be/FhrsUCICh-Y Next video: https://youtu.be/KnHp1uSm6k0
Views: 429 Leandro Junes

01:41:30
This is a recording of the April 1st NYCBUG Meeting on Random Number Generators. We discussed how to design (and not design) secure Random Number Generators. In particular, we will show attacks on Linux /dev/random, present first theoretical analysis on the Windows 8 RNG Fortuna, and talk about the importance of provable security. We will follow these papers: http://eprint.iacr.org/2013/338 http://eprint.iacr.org/2014/167 Recent and relevant blog posts: https://www.schneier.com/blog/archives/2014/03/the_security_of_7.html https://www.schneier.com/blog/archives/2013/10/insecurities_in.html http://it.slashdot.org/story/13/10/14/2318211/linux-rng-may-be-insecure-after-all Speaker Bio Yevgeniy Dodis is a Professor of computer science at New York University. Dr. Dodis received his summa cum laude Bachelors degree in Mathematics and Computer Science from New York University in 1996, and his PhD degree in Computer Science from MIT in 2000. Dr. Dodis was a post-doc at IBM T.J.Watson Research center in 2000, and joined New York University as an Assistant Professor in 2001. He was promoted to Associate Professor in 2007 and Full Professor in 2012. Dr. Dodis' research is primarily in cryptography and network security. In particular, he worked in a variety of areas including leakage-resilient cryptography, cryptography under weak randomness, cryptography with biometrics and other noisy data, hash function and block cipher design, protocol composition and information-theoretic cryptography. Dr. Dodis has more than 100 scientific publications at various conferences, journals and other venues, was the Program co-Chair for the 2015 Theory of Cryptography Conference, has been on program committees of many international conferences (including FOCS, STOC, CRYPTO and Eurocrypt), and gave numerous invited lectures and courses at various venues. Dr. Dodis is the recipient of National Science Foundation CAREER Award, Faculty Awards from IBM, Google and VMware, and Best Paper Award at 2005 Public Key Cryptography Conference. As an undergraduate student, he was also a winner of the US-Canada Putnam Mathematical Competition in 1995.
Views: 1009 BSDTV

01:10:43
- surveillance - choke point - need to know - don't do crypto yourself Cryptographic primitives - hash functions and their basic properties - pseudo-random number generators - determinism - period - entropy - /dev/random vs /dev/urandom
Views: 264 ralienpp

16:54
This video is part of an online course, Applied Cryptography. Check out the course here: Random vs. Pseudorandom Number Generators Watch the next lesson: Cryptographically secure pseudorandom number generator A cryptographically secure pseudo-random number generator (CSPRNG) or cryptographic pseudo-random number generator (CPRNG) is a pseudo-rando.
Views: 11 Shira Hohn

01:20
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 2487 Udacity

01:38:53
Secure Random By Default Dan Kaminsky Chief Scientist, White Ops As a general rule in security, we have learned that the best way to achieve security is to enable it by default. However, across operating systems and languages, random number generation is always exposed via two separate and most assuredly unequal APIs -- insecure and default, and secure but obscure. Why not fix this? Why not make JavaScript and PHP and Java and Python and even libc rand() return strong entropy? What are the issues stopping us? Should we just shell back to /dev/urandom, or is there merit to userspace entropy gathering? How does fork() and virtualization impact the question? What of performance, and memory consumption, and headless machines? Turns out the above questions are not actually rhetorical. Just because a change might be a good idea doesn't mean it's a simple one. This will be a deep dive, but one that I believe will actually yield a fix for the repeated *real world* failures of random number generation systems. Dan Kaminsky has been a noted security researcher for over a decade, and has spent his career advising Fortune 500 companies such as Cisco, Avaya, and Microsoft.Dan spent three years working with Microsoft on their Vista, Server 2008, and Windows 7 releases. Dan is best known for his work finding a critical flaw in the Internet’s Domain Name System (DNS), and for leading what became the largest synchronized fix to the Internet’s infrastructure of all time. Of the seven Recovery Key Shareholders who possess the ability to restore the DNS root keys, Dan is the American representative. Dan is presently developing systems to reduce the cost and complexity of securing critical infrastructure.
Views: 23839 DEFCONConference

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A high-speed quantum random number generator prototype is presented. It can generate truly random numbers based on the fundamental indeterminism of quantum physics. For more information about this technology, please refer to the scientific publications: B. Qi, et al., Optics Letters, 35, 312--314, (2010); F. Xu, et al., Optics Express, 20, 12366--12377, (2012).
Views: 4421 Feihu Xu

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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: 2913 White Hat Cal Poly

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Tom Marble http://debconf14-video.debian.net/video/274/security-not-by-chance-the-altusmetrum-hardware https://summit.debconf.org/debconf14/meeting/20/security-not-by-chance-the-altusmetrum-hardware-true-random-number-generator/ Many elements of security we rely on such as generating of encryption keys and synthesizing one time session keys depend on random number generation. Any predictability of these numbers introduces potential weakness in secure systems. We often use Pseudo-random number generators (PRNGs) because they are quick and convenient, yet they are deterministic algorithms for approximating a sequence of random numbers. By contrast a true random number generator (TRNG) is implemented in hardware based on a physical process that creates unpredictable noise. Often entropy from TRNGs is used to seed PRNGs to provide a balance of speed and unpredictability. In this talk I will discuss the USB TRNG project of AltusMetrum to create a fully open source hardware TRNG. Why make yet another TRNG when several are commercially available? Because most existing TRNGs are expensive, out-of-stock or based on closed designs. The USB TRNG can be connected to the Entropy Key Daemon (ekeyd) which can provide entropy directly to the kernel pool or serving via the EGD protocol. How can we evaluate the quality of the USB TRNG? Results of statistical analysis will provided along with detailed design documents in order to encourage critical community review.
Views: 252 Next Day Video

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: 2264 KOLT KU

01:04
Views: 70 Mat Crossen

18:10
Short introduction to challenges of generating random numbers for cryptography. Course material via: http://sandilands.info/sgordon/teaching
Views: 336 Steven Gordon

01:22
EVERYWHERE IN YOUR LIFE, EYL Lately, as the frequency of threats to data and personal information has been increasing, the security of encryption keys has become crucially important for the perfect security in all areas of information and communication industry. Encryption keys are composed of random numbers that should be impossible to decipher nor predict. Existing Pseudo-random number imitates perfect random number with its generated values from an algorithm that is predictable and vulnerable to hacking. However, EYL will provide perfect random numbers with the world's first encryption technology that utilizes Quantum-random number generator. Since Quantum-random number generator has a mechanism of producing random numbers from detecting the particles emitted randomly and naturally from the radioactive isotopes. EYL provides the perfect encryption keys that even the best hacker cannot even break. As the number of IoT devices is growing exponentially with threatening security risks in reality EYL will provide the perfect security through the encryption technology utilizing quantum-random numbers. In the future, EYL's QRNG, smaller in size with stronger security, will protect your daily lives. QUANTUM SECURITY WILL BE RIGHT IN YOUR POCKET … … … EYL If you have a question, please email to [email protected]

08:53
Views: 10037 Mobilefish.com

15:46
Random numbers on the blockchain: How to guarantee randomness between multiple parties not trusting each other I will discuss the different techniques used to get random number on the blockchain. The talk will cover the security of the methods from technical and game-theoretical point of views. The first 4 techniques will be literature review. While the “Sequential proof of work” will also cover my own research. Clément Lesaege CTO of Kleros, a crowdsourced dispute resolution Dapp. Clément holds a Master of Science in Computer Science from Georgia Tech. He started playing with blockchain technology in 2013. He has worked as blockchain freelancer and focused on finding vulnerabilities in smart contracts.
Views: 527 Ethereum Foundation

42:23
05 Aug 2014 - Bsides Las Vegas 2014 Joe "moloch" - Bishop Fox Dan "AltF4" Petro - Bishop Fox http://www.bishopfox.com http://www.bishopfox.com/blog/2014/08/untwisting-mersenne-twister-killed-prng/ http://www.irongeek.com/i.php?page=videos/bsideslasvegas2014/bg04-untwisting-the-mersenne-twister-how-i-killed-the-prng-moloch Untwisting The Mersenne Twister: How I killed the PRNG Applications rely on generating random numbers to provide security, and fail catastrophically when these numbers turn out to be not so “random.” For penetration testers, however, the ability to exploit these systems has always been just out of reach. To solve this problem, we’ve created “untwister:” an attack tool for breaking insecure random number generators and recovering the initial seed. We did all the hard math, so you don't have to! Random numbers are often used in security contexts for generating unique IDs, new passwords for resets, or cryptographic nonces. However, the built-in random number generators for most languages and frameworks are insecure, leaving applications open to a series of previously theoretical attacks. Lots of papers have been written on PRNG security, but there's still almost nothing practical you can use as a pentester to actually break live systems in the wild. This talk focuses on weaponizing what used to be theoretical into our tool: untwister. Let's finally put rand() to rest. DISCLAIMER: This video is intended for pentesting training purposes only.
Views: 3642 Bishop Fox

02:36
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 1279 Udacity

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: 1892 intrigano

04:01
Subscribe and Help Me Hit 2,700,000 little cuties! ^^ Watch Me React To Texts People Received From Random Numbers! Kyuties! Can we get this video to 5K LIKES?! I love you! ^_^ ♡ OPEN ♡ All rights go to the content creators, if there are any problems, private message me via YouTube and we can solve it together! ^^ ♡ Social Media ♡ ►Instagram https://www.instagram.com/kyutiee_/ ►Twitter https://twitter.com/KyutieOfficial ►Snapchat https://www.snapchat.com/add/kyuutie ►Facebook https://www.facebook.com/KyutieOfficial ♡ SEND ME STUFF! ♡ ► PO BOX 2350 BERALA NSW 2141 AUSTRALIA ♡ VLOG CHANNEL ♡ ►https://www.youtube.com/channel/UCFWBLDCX-hU3EVWrrKfnUhw ---------------------------- ♡ Subscribe To My Beauty Channel ♡ ►https://www.youtube.com/channel/UCGZl9CH01SXA352prkYNO-w Fair Use: For educational purposes and criticism.
Views: 126248 Kyutie

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: 19877 Steven Gordon

00:45
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: 63 Roselyn Wnuk Tipz

02:41
Views: 341 KelliKOnline

03:59
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: 1165735 Tom Scott

08:06
After 6 left doors chosen (amongst many more lefts in the night), we reached the end. The random number generator was definitely not "random" or even "pseudo-random". The RNG is wacked and not cryptographically secure random; but it was fun.
Views: 2 Kamuro Ishigami

16:31
RSA Public Key Encryption Algorithm (cryptography). How & why it works. Introduces Euler's Theorem, Euler's Phi function, prime factorization, modular exponentiation & time complexity. Link to factoring graph: http://www.khanacademy.org/labs/explorations/time-complexity
Views: 492989 Art of the Problem

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: 250 intrigano

46:14
Meltem Sönmez Turan, John Kelsey, and Kerry McKay Cryptographic primitives need random numbers to protect your data. Random numbers are used for generating secret keys, nonces, random paddings, initialization vectors, salts etc. Deterministic pseudorandom number generators are useful, but they still need truly random seeds generated by entropy sources in order to produce random numbers. Researchers have shown examples of deployed systems that did not have enough randomness in their entropy sources, and as a result, crypto keys were compromised. So how do you know how much entropy is in your entropy source? Estimating entropy is a difficult (if not impossible) problem, and we've been working to create usable guidance that will give conservative estimates on the amount of entropy in an entropy source. We want to share some of the challenges and proposed methods. We will also talk about some new directions that we're investigating, and present results of our estimation methods on simulated entropy sources. The authors work within the Cryptographic Technology Group at the National Institute of Standards and Technology (NIST). Meltem is a cryptographer at NIST and holds a Ph.D. in Cryptography from Middle East Technical University. Kerry is a computer scientist at NIST and holds a D.Sc. in Computer Science from The George Washington University. John is an experienced cryptographer at NIST and has degrees in Computer Science and Economics from the University of Missouri Columbia.
Views: 254 Michail S