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Pseudorandom number generators | Computer Science | Khan Academy
 
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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. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 155597 Khan Academy Labs
The Lava Lamps That Help Keep The Internet Secure
 
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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: 1237280 Tom Scott
IOTA tutorial 3: IOTA Seed
 
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If you like this video and want to support me, go this page for my donation crypto addresses: https://www.youtube.com/c/mobilefish/about This is part 3 of the IOTA tutorial. In this video series different topics will be explained which will help you to understand IOTA. It is recommended to watch each video sequentially as I may refer to certain IOTA topics explained earlier. An IOTA seed is 81 characters long and only consists of the latin alphabet characters and the number 9: ABCDEFGHIJKLMNOPQRSTUVWXYZ9 The characters A-Z are all upper case. With the seed the IOTA wallet can generate corresponding addresses. Each specific seed generate addresses belonging to the seed. An IOTA seed looks like: C9RQFODNSAEOZVZKEYNVZDHYUJSA9QQRCUJVBJD9KHAKPTAKZSNNKLJHEFFVK9AWVDAUJRYYKHGWQIAWT According to the official IOTA knowledge base: https://kb.helloiota.com/KnowledgebaseArticle50005.aspx you can use the following methods to generate IOTA seeds: - Linux Operating System: Open a terminal and enter the following command: cat /dev/urandom |tr -dc A-Z9|head -c${1:-81} - Mac Operating System: Open a terminal and enter the following command: cat /dev/urandom |LC_ALL=C tr -dc 'A-Z9' | fold -w 81 | head -n 1 The function /dev/urandom creates cryptographically random numbers by gathering random data for example environmental noise (entropy) from device drivers, network packet timings and other sources into an entropy pool. The data from the entropy pool is used as input for the Cryptographically Secure PseudoRandom Number Generator (CSPRNG) This generator will generate the random numbers. urandom means unlimited random On the Mac there is no difference between /dev/random and /dev/urandom, both behave identically. On Linux systems there are differences between /dev/random and /dev/urandom. In this presentation these differences will not be discussed. Another solution the IOTA knowledge base recommends to generate an IOTA seed is using this web application: https://ipfs.io/ipfs/QmdqTgEdyKVQAVnfT5iV4ULzTbkV4hhkDkMqGBuot8egfA The source code for this seed generator can be found at: https://github.com/knarz/seedgen The knarz/seedgen uses the Stanford Javascript Crypto Library. This library can be found at: https://github.com/bitwiseshiftleft/sjcl More information about this library can be found at: http://bitwiseshiftleft.github.io/sjcl/ http://bitwiseshiftleft.github.io/sjcl/doc The Stanford Javascript Crypto Library (SJCL) is a project by the Stanford Computer Security Lab to build a secure, powerful, fast, small, easy-to-use, cross-browser library for cryptography in Javascript. The SJCL library is used in many web applications. If you want to use the web application to generate an IOTA seed do the following: - Goto https://ipfs.io/ipfs/QmdqTgEdyKVQAVnfT5iV4ULzTbkV4hhkDkMqGBuot8egfA and save the webpage locally on your computer. - Disconnect your computer from the Internet (disable WiFi, or remove your Ethernet cable) - Open the webpage and move your mouse until its reaches 100% - Store your IOTA seed in a secure location. You should NEVER create an IOTA seed by entering 81 characters (A-Z9) yourself on a keyboard. You should NEVER create an IOTA seed using an web application while you are online. You should NEVER use unknown IOTA seed generators. Use the seed generators recommended by the official IOTA knowledge base: https://kb.helloiota.com/KnowledgebaseArticle50005.aspx There are several online IOTA seed generators which do not generate Cryptographically Secure Random Numbers which means there is big chance someone else can generate the same seed as you have. Check out all my other IOTA tutorial videos https://goo.gl/aNHf1y Subscribe to my YouTube channel: https://goo.gl/61NFzK The presentation used in this video tutorial can be found at: https://www.mobilefish.com/developer/iota/iota_quickguide_tutorial.html #mobilefish #howto #iota
Views: 10616 Mobilefish.com
How secure is 256 bit security?
 
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Supplement to the cryptocurrency video: How hard is it to find a 256-bit hash just by guessing and checking? What kind of computer would that take? Cryptocurrency video: https://youtu.be/bBC-nXj3Ng4 Thread for Q&A questions: http://3b1b.co/questions Several people have commented about how 2^256 would be the maximum number of attempts, not the average. This depends on the thing being attempted. If it's guessing a private key, you are correct, but for something like guessing which input to a hash function gives a desired output (as in bitcoin mining, for example), which is the kind of thing I had in mind here, 2^256 would indeed be the average number of attempts needed, at least for a true cryptographic hash function. Think of rolling a die until you get a 6, how many rolls do you need to make, on average? Music by Vince Rubinetti: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 868405 3Blue1Brown
Defeat 2FA token because of bad randomness - rhme2 Twistword (Misc 400)
 
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Generating random numbers on computers is not easy. And while the intended solution was really hard, the challenge had a problem with the random number generation, which allowed me to solve it. Clarification from Andres Moreno (riscure) on the challenge: "The "official" challenge solution involved reading the tiny Mersenne twister (tinyMT) paper, writing some equations, and using a solver. The tinyMT is tricky to initialize. Giving a proper seed is not enough. You need to provide initial state matrices with certain properties (there is a generator for this). The challenge used improper initialized matrices (zeros) that reduced the PRNG period. During tests, we found that ~12hr were needed to solve the challenge (solver time only), but we did not test the amount of entropy reduction by improper state initialization. Fortunately, the problem was not in the PRNG." -------------------------------------- Twitter: https://twitter.com/LiveOverflow Website: http://liveoverflow.com/ Subreddit: https://www.reddit.com/r/LiveOverflow/
Views: 15625 LiveOverflow
COSIC Seminar - Entropy Sources For Cryptographic Random Number Generation (John Kelsey)
 
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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.
[wr0ng 2017] Security of Pseudo-Random Number Generators With Input - Damien Vergnaud
 
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A pseudo-random number generator (PRNG) is a deterministic algorithm that produces numbers whose distribution is indistinguishable from uniform. A formal security model for PRNG with input was proposed in 2005 by Barak and Halevi. This model involves an internal state that is refreshed with a (potentially biased) external random source, and a cryptographic function that outputs random numbers from the internal state. In this talk, we will discuss the Barak-Halevi model and its extension proposed in 2013 by Dodis, Pointcheval, Ruhault, Wichs and Vergnaud to include a new security property capturing how a PRNG should accumulate the entropy of the input data into the internal state. We will present analysis of the security of real-life PRNGs in this model and present efficient constructions that achieve provable security.
Views: 147 ECRYPT
Sources Of Randomness - Applied Cryptography
 
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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: 1042 Udacity
Security not by chance: the AltusMetrum hardware true random number generator
 
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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: 258 Next Day Video
Random Number Generation - How does a computer generate random numbers?
 
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~ Be sure to like the video and comment down below over what you would like to see next video. Don't forget to subscribe to the channel to get receive new videos every week! ~ FUN FACTS - Some PRNG's (Pseudo-Random Number Generators) can pass mathematical probability tests. - A common PRNG seed is "Xsub(n+1) = (a * (Xsub(n)) mod m", when "a and b are large integers", and m is the maximum number being generated SOURCES https://www.random.org/ https://en.wikipedia.org/wiki/Random_number_generation
Views: 1944 Computer Central
Pseudo Random Number Generators (CSS322, Lecture 7, 2013)
 
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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: 20898 Steven Gordon
Prng Implementation - Applied Cryptography
 
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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: 3049 Udacity
What is PSEUDORANDOM NUMBER GENERATOR? What does PSEUDORANDOM NUMBER GENERATOR mean?
 
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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: 2738 The Audiopedia
How to Generate Pseudorandom Numbers | Infinite Series
 
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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: 101088 PBS Infinite Series
Pseudo Random Number Generator - Applied Cryptography
 
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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: 8004 Udacity
1. Introduction, Threat Models
 
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MIT 6.858 Computer Systems Security, Fall 2014 View the complete course: http://ocw.mit.edu/6-858F14 Instructor: Nickolai Zeldovich In this lecture, Professor Zeldovich gives a brief overview of the class, summarizing class organization and the concept of threat models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 354619 MIT OpenCourseWare
Python Random Number Generator: the Random Module  ||  Python Tutorial  ||  Learn Python Programming
 
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To generate random numbers in Python, you use the Random Module. This contains functions for generating random numbers from both continuous and discrete distributions. In this video, we will cover the key random number generators. ➢➢➢➢➢➢➢➢➢➢ To learn Python, you can watch our playlist from the beginning: https://www.youtube.com/watch?v=bY6m6_IIN94&list=PLi01XoE8jYohWFPpC17Z-wWhPOSuh8Er- ➢➢➢➢➢➢➢➢➢➢ We recommend: Python Cookbook, Third edition from O’Reilly http://amzn.to/2sCNYlZ The Mythical Man Month - Essays on Software Engineering & Project Management http://amzn.to/2tYdNeP Shop Amazon Used Textbooks - Save up to 90% http://amzn.to/2pllk4B ➢➢➢➢➢➢➢➢➢➢ Subscribe to Socratica: http://bit.ly/1ixuu9W To support more videos from Socratica, visit Socratica Patreon https://www.patreon.com/socratica Socratica Paypal https://www.paypal.me/socratica We also accept Bitcoin! :) Our address is: 1EttYyGwJmpy9bLY2UcmEqMJuBfaZ1HdG9 ➢➢➢➢➢➢➢➢➢➢ Python instructor: Ulka Simone Mohanty Written & Produced by Michael Harrison FX by Andriy Kostyuk
Views: 77461 Socratica
Salted Password Scheme - Applied Cryptography
 
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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: 13103 Udacity
Elliptic Curve Back Door - Computerphile
 
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The back door that may not be a back door... The suspicion about Dual_EC_DRBG - The Dual Elliptic Curve Deterministic Random Bit Generator - with Dr Mike Pound. EXTRA BITS: https://youtu.be/XEmoD06_mZ0 Nothing up my sleeve Numbers: https://youtu.be/oJWwaQm-Exs Elliptic Curves: https://youtu.be/NF1pwjL9-DE https://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: https://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
Views: 161641 Computerphile
DEFCON 17: Design and Implementation of a Quantum True Random Number Generator
 
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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: 5202 Christiaan008
DEF CON 22 - Dan Kaminsky - Secure Random by Default
 
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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: 39911 DEFCONConference
A Quantum Random Number Generator for cryptographic applications
 
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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: 621 BTYoungScientists
How Random Is Your RNG [ShmooCon 2015]
 
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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: 285 Michail S
PRNG Implementation Solution - Applied Cryptography
 
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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: 1335 Udacity
How do we know our PRNGs work properly? (33c3)
 
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https://media.ccc.de/v/33c3-8099-how_do_we_know_our_prngs_work_properly Pseudo-random number generators (PRNGs) are critical pieces of security infrastructure. Yet, PRNGs are surprisingly difficult to design, implement, and debug. The PRNG vulnerability that we recently found in GnuPG/Libgcrypt (CVE-2016-6313) survived 18 years of service and several expert audits. In this presentation, we not only describe the details of the flaw but, based on our research, explain why the current state of PRNG implementation and quality assurance downright provokes incidents. We also present a PRNG analysis method that we developed and give specific recommendations to implementors of software producing or consuming pseudo-random numbers to ensure correctness. Vladimir Klebanov Felix Dörre
Views: 999 media.ccc.de
The Randomness Problem: How Lava Lamps Protect the Internet
 
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Go to https://Brilliant.org/SciShow to get 20% off of an annual Premium subscription! Randomness is important for all kinds of things, from science to security, but to generate true randomness, engineers have turned to some pretty odd tricks! Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, D.A. Noe, الخليفي سلطان, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.wired.com/story/cloudflare-lava-lamps-protect-from-hackers/ https://sploid.gizmodo.com/one-of-the-secrets-guarding-the-secure-internet-is-a-wa-1820188866 https://www.fastcompany.com/90137157/the-hardest-working-office-design-in-america-encrypts-your-data-with-lava-lamps https://www.nytimes.com/2001/06/12/science/connoisseurs-of-chaos-offer-a-valuable-product-randomness.html https://blog.cloudflare.com/why-randomness-matters/ https://www.design-reuse.com/articles/27050/true-randomness-in-cryptography.html https://www.random.org/randomness/ https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-856j-randomized-algorithms-fall-2002/lecture-notes/ https://link.springer.com/chapter/10.1007/978-3-319-26300-7_3 https://www.maa.org/sites/default/files/pdf/upload_library/22/Ford/Volchan46-63.pdf https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-22r1a.pdf http://www.iro.umontreal.ca/~simardr/testu01/guideshorttestu01.pdf https://www.rand.org/pubs/monograph_reports/MR1418/index2.html https://www.rand.org/content/dam/rand/pubs/papers/2008/P113.pdf https://docs.microsoft.com/en-us/windows/desktop/secauthn/tls-handshake-protocol https://tools.ietf.org/html/rfc2246#page-47 https://ops.fhwa.dot.gov/trafficanalysistools/tat_vol3/vol3_guidelines.pdf https://ocw.mit.edu/courses/aeronautics-and-astronautics/16-36-communication-systems-engineering-spring-2009/lecture-notes/MIT16_36s09_lec21_22.pdf https://telescoper.wordpress.com/2009/04/04/points-and-poisson-davril/ https://auto.howstuffworks.com/remote-entry2.htm https://web.archive.org/web/20070315010555/https://cigital.com/papers/download/developer_gambling.php Images: https://commons.wikimedia.org/wiki/File:Middle-square_method.svg https://www.youtube.com/watch?v=zdW6nTNWbkc https://commons.wikimedia.org/wiki/File:Sun-crypto-accelerator-1000.jpg
Views: 326563 SciShow
Lecture 4: Stream Ciphers and Linear Feedback Shift Registers by Christof Paar
 
01:29:40
For slides, a problem set and more on learning cryptography, visit www.crypto-textbook.com
NMCS4ALL: Random number generators
 
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: 26156 Dave Ackley
Pseudo Random Number Generator Solution - Applied Cryptography
 
01:20
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 2609 Udacity
Micro Quantum Random Number Generator
 
01:03
A tiny chip(5mm*5mm) generates Random Numbers which is Micro Quantum Random Number Generator(QRNG) using randomness of radioactive alpha particle. The Micro-QRNG is very small, cheap, unbiased, unpredictable, uncorrelated and harmless. Ultra speed USB Dongle type Micro-QRNG(over 1Gbps) and thin film type QRNG are under developing. Contact : www.eylpartners.com, [email protected](Korea)
Pseudorandom number generator
 
16:35
Please give us a THUMBS UP if you like our videos!!! Source:http://en.wikipedia.org/wiki/Pseudorandom_number_generator 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.
Views: 414 Wikivoicemedia
Cloudflare​ ​Lava Lamp​ ​Entropy
 
05:12
Steve Gibson describes how Cloudflare uses a wall of lava lamps to create entropy: a high resolution camera takes image frames of the lava lamp-filled wall every millisecond. The images are hashed to produce absolutely unpredictable truly random - non-algorithm-based - numbers. Watch the full episode: https://twit.tv/sn/625 Subscribe: https://twit.tv/subscribe About us: TWiT.tv is a technology podcasting network located in the San Francisco Bay Area with the #1 ranked technology podcast This Week in Tech hosted by Leo Laporte. Every week we produce over 30 hours of content on a variety of programs including Tech News Today, The New Screen Savers, Mac Break Weekly, This Week in Google, Windows Weekly, Security Now, All About Android, and more. Follow us: https://twit.tv https://twitter.com/twit https://www.facebook.com/TWiTNetwork https://www.instagram.com/twit.tv
Views: 2637 TWiT Netcast Network
the life of straight-ups RNG Mersenne Twister
 
02:47
shows absolute rates see more info here: https://en.youroul.com/forum/82-1-VIDEO-the-life-of-straight-ups
Views: 449 youroul.com
EC08 Rump 19, Alexander May
 
02:46
Eurocrypt 2009, Alexander May
Views: 726 James Hughes
Bad Seed (Babs Seed Parody)
 
01:48
Most people think of the "black sheep" of the family when they hear the phrase "Bad seed". I think of an insecurely initialized PseudoRandom Number Generator. Take from that what you will. I'm not completely sure that the lyrics in this song are accurate, unfortunately... Take this more as a work in progress. I'll probably upload a fixed version later, likely with a video once I have more time to get it made. Background art is a modified version of the image found at http://rainbowplasma.deviantart.com/art/Babs-Seed-Background-340372531 which is licensed under Creative Commons "Attribution 3.0" http://creativecommons.org/licenses/by/3.0 8 Bit backing: https://www.youtube.com/watch?v=14yim_M1CcU Original song (Babs Seed) written by Daniel Ingram for My Little Pony: Friendship is Magic
Views: 1511 Jon4270
Microsoft CryptoAPI
 
03:51
The Cryptographic Application Programming Interface (also known variously as CryptoAPI, Microsoft Cryptography API, MS-CAPI or simply CAPI) is an application programming interface included with Microsoft Windows operating systems that provides services to enable developers to secure Windows-based applications using cryptography. It is a set of dynamically linked libraries that provides an abstraction layer which isolates programmers from the code used to encrypt the data. The Crypto API was first introduced in Windows NT 4.0 and enhanced in subsequent versions. CryptoAPI supports both public-key and symmetric key cryptography, though persistent symmetric keys are not supported. It includes functionality for encrypting and decrypting data and for authentication using digital certificates. It also includes a cryptographically secure pseudorandom number generator function CryptGenRandom. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 2561 Audiopedia
Pseudorandom function family
 
04:11
Pseudorandom function family In cryptography, a pseudorandom function family, abbreviated PRF, is a collection of efficiently-computable functions which emulate a random oracle in the following way: no efficient algorithm can distinguish (with significant advantage) between a function chosen randomly from the PRF family and a random oracle (a function whose outputs are fixed completely at random).Pseudorandom functions are vital tools in the construction of cryptographic primitives, especially secure encryption schemes. -Video is targeted to blind users Attribution: Article text available under CC-BY-SA image source in video https://www.youtube.com/watch?v=29beT9_LR38
Views: 809 WikiAudio
Create a Random Number in C++
 
06:24
Demonstrates how to use rand() and srand() to generate random numbers. The software used in this tutorial is Xcode, but the code can be applied to any C++ compiler. Table of Contents: 00:26 - rand() function 01:11 - cstdlib header file 01:49 - Pseudorandom Number 02:39 - time() function 03:18 - ctime header file 03:32 - srand() function
Views: 14447 profgustin
BIVBlog #31: More news on the Hardware Random Number Generator
 
20:42
Some crazy things have happened since the previous episode: There were rather mysterious test results with different Zener diodes that took me some time to figure out, I'm actively joining forces with the Cryptech project and I've ordered the first generation of proper test PCBs among other things. References and discussion forum at http://www.stepladder-it.com/bivblog/31
Linear congruential generator
 
10:38
A linear congruential generator is an algorithm that yields a sequence of pseudo-randomized numbers calculated with a discontinuous piecewise linear equation. The method represents one of the oldest and best-known pseudorandom number generator algorithms. The theory behind them is relatively easy to understand, and they are easily implemented and fast, especially on computer hardware which can provide modulo arithmetic by storage-bit truncation. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1681 Audiopedia
Bitcoin Hardware Wallet - BitStash
 
01:47
BitStash is a ultra secure bitcoin wallet So, why BitStash? BitStash is a global solution that is accessible to everyone, everywhere No third parties between you & your Bitcoin - decreases chances of theft & seizure No business continuity or asset freezing risks Use from mobile, laptop & desktop devices via simple to use, awesome wallet applications PLUS true cold storage - keep large balances in your safe deposit box Multiple accounts possible - the whole family can use Bitcoin securely Set account spending limits, mobile wallet limits & automatic balance management BIP32 keys new address for each transaction assist in maintaining privacy Full support for Bitcoin, Litecoin & Dogecoin with more cryptocurrencies in the future Build a new BitStash anytime from Cold Storage keys & password How easy is it to use? Designed to be incredibly easy to use Integrated BIP70 payment protocol support, makes spending Bitcoin as simple as online shopping Mobile wallet for spending on the go - secures small balances, tops up from device wallet Realtime balances displayed in over 100 Fiat currencies Off blockchain notes & merchant information shared via BitStash™ with mobile & desktop apps Simple to use Cold Storage. Multiple Cold Storage & backups can be made Build a new BitStash™ anytime from your Cold Storage keys & password Real Time balances displayed in over 100 Fiat currencies Check out the screen shots to see for yourself HOW SECURE? All keys generated in device, meaning device keys are never exposed to malware risks Keys stored encrypted on the device with user password, PBKDF2 extended with 2k rounds Keys derived from atmospheric noise, PBKDF2 extended with a PRNG seed on initial setup Hardened bluetooth protocol prevents message capture & replay Combination of message signing & rolling codes ensure message authenticity Only paired & authenticated devices can successfully send messages Additional AES message encryption with Diffie-Hillman Key Agreement Transaction signing takes place in the device Hardened USB circuitry, inoculated by design from BadUSB malware. Physical anti tamper & self-destruct circuitry, rebuild from cold storage backup Designed to meet FIPS 140-2 level 3 certification. What about Malware on the client computer? Unique 'COLOR CAPTCHA' using BitStash™ color LEDs used in desktop only device mode 2 Factor authentication enabled with second, physically present, registered mobile device IOS8 Touch ID support, use fingerprint identification on compatible iPhones in mobile & 2factor transactions No reliance on sms infrastructure, 2 factor authentication for everyone Configurable auto sleep on three invalid attempts Configurable auto destruct on N invalid attempts, build a new BitStash from Cold Storage keys & password For more information visit: https://bitstash.com ______________________________________________ See also bitcoin smartcard ledger wallet nano: https://www.youtube.com/watch?v=5Ss7xnFP9AM ______________________________________________ Don't forget Subsribe to our channel: https://www.youtube.com/channel/UCOh4dka-cRhc0Yl8820mCxg ______________________________________________ This video: http://youtu.be/cSS2h3guXpc
Views: 15499 Devices For Bitcoin
Pseudorandom Password Generator
 
00:28
http://demonstrations.wolfram.com/PseudorandomPasswordGenerator/ The Wolfram Demonstrations Project contains thousands of free interactive visualizations, with new entries added daily. Pseudorandom passwords are generated by selecting a password length and a range of characters. Click a particular character to include or exclude it. These selections are encoded in the alphanumeric string at the bottom. This string can be used to speci... Contributed by: Michael Schreiber
Views: 277 wolframmathematica

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