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: 158498 Khan Academy Labs
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: 1284342 Tom Scott
Views: 10946 Mobilefish.com
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 8475 Udacity
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.
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: 5381 Christiaan008
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: 3031 The Audiopedia
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: 162 ECRYPT
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: 651 BTYoungScientists
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 1370 Udacity
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: 991198 3Blue1Brown
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: 105255 PBS Infinite Series
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: 261 Next Day Video
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
Views: 236 Benedikt Stockebrand
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." -=[ 🔴 Stuff I use ]=- → Microphone:* https://amzn.to/2LW6ldx → Graphics tablet:* https://amzn.to/2C8djYj → Camera#1 for streaming:* https://amzn.to/2SJ66VM → Lens for streaming:* https://amzn.to/2CdG31I → Connect Camera#1 to PC:* https://amzn.to/2VDRhWj → Camera#2 for electronics:* https://amzn.to/2LWxehv → Lens for macro shots:* https://amzn.to/2C5tXrw → Keyboard:* https://amzn.to/2LZgCFD → Headphones:* https://amzn.to/2M2KhxW -=[ ❤️ Support ]=- → per Video: https://www.patreon.com/join/liveoverflow → per Month: https://www.youtube.com/channel/UClcE-kVhqyiHCcjYwcpfj9w/join -=[ 🐕 Social ]=- → Twitter: https://twitter.com/LiveOverflow/ → Website: https://liveoverflow.com/ → Subreddit: https://www.reddit.com/r/LiveOverflow/ → Facebook: https://www.facebook.com/LiveOverflow/ -=[ 📄 P.S. ]=- All links with "*" are affiliate links. LiveOverflow / Security Flag GmbH is part of the Amazon Affiliate Partner Programm. #CTF #Cryptography
Views: 18934 LiveOverflow
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: 84969 Socratica
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: 383991 SciShow
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: 1021 media.ccc.de
~ 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: 2138 Computer Central
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: 179636 Computerphile
This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Hardware_random_number_generator 00:03:17 1 Uses 00:04:11 1.1 Cryptography 00:05:29 2 Early work 00:07:57 3 Physical phenomena with random properties 00:08:08 3.1 Quantum random properties 00:11:19 3.2 Classical random properties 00:13:46 3.2.1 Clock drift 00:17:44 4 Dealing with bias 00:18:40 4.1 Software whitening 00:23:19 4.2 PRNG with periodically refreshed random key 00:24:00 5 Using observed events 00:27:22 6 Problems 00:28:20 6.1 Attacks 00:28:56 6.2 Estimating entropy 00:29:35 6.3 Performance test 00:31:35 7 See also Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts Speaking Rate: 0.7945921940284152 Voice name: en-US-Wavenet-F "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= In computing, a hardware random number generator (HRNG) or true random number generator (TRNG) is a device that generates random numbers from a physical process, rather than by means of an algorithm. Such devices are often based on microscopic phenomena that generate low-level, statistically random "noise" signals, such as thermal noise, the photoelectric effect, involving a beam splitter, and other quantum phenomena. These stochastic processes are, in theory, completely unpredictable, and the theory's assertions of unpredictability are subject to experimental test. This is in contrast to the common paradigm of pseudo-random number generation commonly implemented in computer programs or cryptographic hardware. A hardware random number generator typically consists of a transducer to convert some aspect of the physical phenomena to an electrical signal, an amplifier and other electronic circuitry to increase the amplitude of the random fluctuations to a measurable level, and some type of analog to digital converter to convert the output into a digital number, often a simple binary digit 0 or 1. By repeatedly sampling the randomly varying signal, a series of random numbers is attained. The main application for electronic hardware random number generators is in cryptography, where they are used to generate random cryptographic keys to transmit data securely. They are widely used in Internet encryption protocols such as Secure Sockets Layer (SSL). Random number generators can also be built from "random" macroscopic processes, using devices such as coin flipping, dice, roulette wheels and lottery machines. The presence of unpredictability in these phenomena can be justified by the theory of unstable dynamical systems and chaos theory. Even though macroscopic processes are deterministic under Newtonian mechanics, the output of a well-designed device like a roulette wheel cannot be predicted in practice, because it depends on the sensitive, micro-details of the initial conditions of each use. Although dice have been mostly used in gambling, and as "randomizing" elements in games (e.g. role playing games), the Victorian scientist Francis Galton described a way to use dice to explicitly generate random numbers for scientific purposes in 1890.Hardware random number generators generally produce only a limited number of random bits per second. In order to increase the available output data rate, they are often used to generate the "seed" for a faster cryptographically secure pseudorandom number generator, which then generates a pseudorandom output sequence at a much higher data rate.
Views: 3 Subhajit Sahu
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: 27124 Dave Ackley
Views: 43422 DEFCONConference
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 2678 Udacity
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: 21434 Steven Gordon
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
A Google TechTalk, 2018-12-05, presented by Alessandro Barenghi ABSTRACT: This talk will present LEDAkem and LEDApkc, a key agreement scheme and a public key encryption scheme resistant against attacks with both classical and quantum computers. In this talk I will present the schemes and report recent results on how we can automatically generate key sizes and cryptosystem parameters tailored for a desired security level, providing practical performance figures. About the speaker: Alessandro Barenghi is currently assistant professor at Politecnico di Milano, and one of the proposers of the LEDAkem/LEDApkc cryptoschemes to the NIST post-quantum standardization initiative.
Views: 1023 GoogleTechTalks
David Bismark gives a short and poetic talk on the rarity of true randomness. A lot of the time what seems random is based on probability and comparative deductions, but pure randomness can only be found in nature. TEDArchive presents previously unpublished talks from TED conferences. Enjoy this unedited talk by David Bismark. Filmed at TEDGlobal University 2012. NOTE: Comments are disabled on this video. We made this difficult decision for the TED Archive because we believe that a well-moderated conversation allows for better commentary from more people and more viewpoints. Studies show that aggressive and hateful comments silence other commenters and drive them away; unfortunately, YouTube's comment moderation tools are simply not up to the task of allowing us to monitor comments on so many videos at once. (We'd love to see this change, YouTube.) So for now, if you'd like to comment on this talk, please use Facebook, Twitter or G+ to discuss with your networks.
Views: 97 TED Archive
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: 373575 MIT OpenCourseWare
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: 15505 Devices For Bitcoin
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: 1513 Jon4270
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: 1761 Audiopedia
If you find our videos helpful you can support us by buying something from amazon. https://www.amazon.com/?tag=wiki-audio-20 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: 840 WikiAudio
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: 2688 Audiopedia
This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/RdRand 00:01:13 1 Overview 00:04:31 1.1 Performance 00:06:23 1.2 Compilers 00:07:09 1.3 Sample x86 asm code to check upon RDRAND instruction 00:07:23 2 Applications 00:08:37 3 Reception 00:10:51 4 See also Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts Speaking Rate: 0.8352576993184428 Voice name: en-GB-Wavenet-A "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= RDRAND (previously known as Bull Mountain) is an instruction for returning random numbers from an Intel on-chip hardware random number generator which has been seeded by an on-chip entropy source. RDRAND is available in Ivy Bridge processors and is part of the Intel 64 and IA-32 instruction set architectures. AMD added support for the instruction in June 2015.The random number generator is compliant with security and cryptographic standards such as NIST SP 800-90A, FIPS 140-2, and ANSI X9.82. Intel also requested Cryptography Research Inc. to review the random number generator in 2012, which resulted in the paper Analysis of Intel's Ivy Bridge Digital Random Number Generator.RDSEED is similar to RDRAND and provides higher level access to the entropy hardware. The RDSEED generator and processor instruction rdseed are available with Intel Broadwell CPUs and AMD Zen CPUs.
Views: 2 wikipedia tts
Cryptographic random numbers Explanation with Dephi source explaining how to generate random numbers with proper sources of entropy and hash functions. [Cryptographic Random Numbers] //Source Code Bellow Simply calling Randomize and using the Random() procedure is a severe security flaw in application seeking to pretect data with random numbers. A random number generator gets is 'randomness' from entropy. Borlands Random() procedure uses a 32bit seed as entropy, and that seed is generated by the Randomize procedure which gets its entropy the system time and date which are very probabilistic and can be tested for quickly. To generate random numbers that cannot be differentiated from pure chaos is a VERY difficult task on a computer, mainly because you rely on internal states that are often too predictable. The idea is to gather entropy from the least predictable states of the system and dillute that entropy inside a much larger pool. The pool I refer to is the internal state of the random number generator. WHAT IT IS: There are important properties that have to be respected when generating random numbers. More specificaly, random numbers intended for encryption. The properties that implicated in this random number gerenartors design are strongly based on Bruce Schneier's Yarrow (www.counterpane.com). The first property is to ensure there is always anought entropy in the pool before outputing random numbers so that the pool never enters a weakened state where the next random numbers that are output have predictible information. The next property comes in handy if you're going to be using the generator to make session keys that will change multiple times during a chat session. It is important that one compromised key will not reveal any of the previous keys nor any of the next keys that will be used. To do this we need to eliminate the mathematical relationship between the random numbers that are output and the state of the pool. The third desired property implies that enven if the entropy gathered from your sources is of poor quality (fairly predictable) the pool must not suffer for the low entropy and the output random numbers must not show any evidence of this. I have tested this unit extensively. The final and most crucial test centered around the third property. To make an extreme case, I started the pool with nothing but zeros in it and generated "12MB (100,000,000 bits). I used the DieHard battery of tests (http://stat.fsu.edu/"geo/diehard.html) and it passed all 15 with flying colors... without collecting any entropy. With this I am satisfied of the random number generator's performance and submit it to you to use as a secure alternative to what is commonly seen in programs. HOW TO IT WORKS: -two entropy gatherers are created:  a thread that tracks mouse movement at random intervals taking 4bits of entropy from the mouse position and state of the system's high-resolution timer.  a latency calculator that gets 4bits of entropy from the high-resolution timer when called by the main app (this is used by alling TKeyGenerator.AddLatency on the OnKeyDown event of an edit box, to count harddrive latency, or irq latency) When either of the entropy gatherers has accumulated 32bits, it sends it to the entropy pool. -The entroyp pool takes in entropy 32bits at a time and uses it to fill an entropy buffer of 256bits, when the buffer is full, a primary reseed is executed. -The primary reseed updates the primary pool (a Hash Context: internal state of a hash function) with the entropy and XORs it with the pool's seed (this seed is used similarly the way randomize generates randseed). After every primary reseed, the seed (with now 256bits of entropy) is ready to be used to output random numbers if the calling application so desires it, but it will continue to reseed and gather entropy regardless regardless of that. After 8 primary reseeds have taken place, a secondary reseed is executed. -The secondary reseed updates the secondary pool with the contents of the primary pool and then flushes the contents of the primary pool into a state with no entropy. The secondary pool is persistant in that it is never flushed and will carry entropy bits from various reseeds. A completly new seed is generated from the secondary reseed (where as the primary on modifies it with entropy). This secondary reseed prevents backtracking properties (gessing previous states of the pool) and ensures there is entropy in the pool even under conditions where new entropy is of poor quality. -When the calling application needs to generate a key it calls SafeGetKey which ensures that no more than 8 sets of 256bits of random numbers can be generated from a single reseed. To do this a key reserve counter is incremented every primary reseed, and cannot exceed 8. When a you generate a set of random numbers the key reserve is decremented and the function will return..
Views: 0 How to : Tips and Trick