Cryptography | Random number generation
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 for as long as an equation governing such phenomena is unknown or uncomputable. This is in contrast to the paradigm of pseudo-random number generation commonly implemented in computer programs. 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 obtained. 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 Transport Layer Security (TLS). 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 is supported 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 can be impractical to predict 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. With random number generators based on a noisy classical system or an elementary quantum measurement, assertions of unpredictability should be based on a careful model describing the underlying physics. Yet any such model must make a number of assumptions that may not be valid, and are difficult to verify. But starting in 2010, "Einstein-certified" quantum physics experiments have been able to demonstrate, sometimes even to remote observers, that the bits they produce are unpredictable, requiring only very mild assumptions about signals not being able to travel faster than the speed of light. (Wikipedia).
Coding Math: Episode 51 - Pseudo Random Number Generators Part I
Back to School Special. This short series will discuss pseudo random number generators (PRNGs), look at how they work, some algorithms for PRNGs, and how they are used. Support Coding Math: http://patreon.com/codingmath Source Code: https://jsbin.com/nifutup/1/edit?js,output Earlier Sourc
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randomGenerator in Number Puzzles
https://teacher.desmos.com/activitybuilder/custom/61282f1b979758794705a7d6
From playlist Computation Layer
Coding Math: Mini #6 - A Better Random
In this mini episode, we create two useful functions for generating random numbers. Support Coding Math: http://patreon.com/codingmath Source Code: http://github.com/bit101/codingmath
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Randomness - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
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Pseudo Random Number Generator Solution - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Randomness Solution - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
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Biased Generator - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
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Discrete Structures: Random number generators, checksums, hashing
Learn about random number generators, checksums, and hashing. We'll start with the von Neumann random number generator and the contemporary linear congruential generator. Then we'll go over the Luhn algorithm used for credit card number validation and then introduce hash functions such as
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EEVBlog #1075 - Digital BitBox Hardware Wallet Review
Review of the Digital BitBox crypto bitcoin hardware wallet, made in Switzerland. Bitcoin donations appreciated: 12gVrtZg75SWQFKhQ512DjPKpSFt3sK218 http://www.MyWallet.ch https://kit.com/EEVblog/crypto-hardware https://shiftcrypto.ch/ Forum: http://www.eevblog.com/forum/blog/eevblog-107
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Stanford Webinar - Latest Developments in Cryptography with Dan Boneh
Learn more at https://online.stanford.edu/programs/stanford-advanced-cybersecurity-certificate?utm_source=youtube&utm_medium=social&utm_campaign=webinar In this webinar, you’ll learn the latest on all things “crypto” from Professor Dan Boneh, head of the Stanford’s applied cryptography gr
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Bitcoin and Open Blockchain Livestream Q&A with Andreas M. Antonopoulos - March 2022
00:00:00 Introductory video and countdown 00:03:10 Welcome and start of stream (with audio issues) 00:05:00 Audio issues fixed! Start of stream again. 00:06:45 I am interested in multisig wallets but communicating instructions to my heirs how to restore that without compromising my securit
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Lecture 4: Primary-Backup Replication
Lecture 4: Primary-Backup Replication MIT 6.824: Distributed Systems (Spring 2020) https://pdos.csail.mit.edu/6.824/
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25c3: Algorithmic Music in a Box
Speaker: Wesen Doing music with microcontrollers Small devices like microcontrollers, coupled to a few buttons, knobs, encoders and LEDs, allow for a host of interesting and creative musical applications. Solder a few bits together, program a few lines, and you can build a deep device to
From playlist 25C3: Nothing to hide
Speakers: djb | Nadia Heninger | Tanja Lange RSA factorization in the real world RSA is the dominant public-key cryptosystem on the Internet. This talk will explain the state of the art in techniques for the attacker to figure out your secret RSA keys. A typical 1024-bit RSA public key
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30C3: Trezor: Bitcoin hardware wallet (EN)
For more information and to download the video visit: http://bit.ly/30C3_info Playlist 30C3: http://bit.ly/30c3_pl Speaker: Pavol "stick" Rusnak TREZOR is a hardware wallet for Bitcoin. We identified security of the end users' computer as one of the main problems that block Bitcoin mass
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Speaker: DAN KAMINSKY CHIEF SCIENTIST, DKH If there's one thing we know, it's that we're doing it wrong. Sacred cows make the best hamburgers, so in this year's talk I'm going to play with some techniques that are obviously wrong and evil and naive. There will also be a lot of very intere
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EEVblog #1062 - Trezor Model T Hardware Wallet Review
Unboxing and review of the new Trezor Model T cryptocurrency bitcoin hardware wallet. And a comparison with the Ledger Nano S. Also a talk on Ethereum contracts, Myetherwaller and ICO's Crypto Currency: https://www.eevblog.com/crypto-currency/ https://kit.com/EEVblog/crypto-hardware Hard
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Shmoocon 2010: Infrastructural Weaknesses in Distributed Wireless Communication Services 2/6
Clip 2/6 Speaker: Zack Fasel In order to combat the problem of wireless communication services in remote and well insulated buildings, wireless communication providers have designed and issued a distributed wireless communication device (DWCD... yes I made that acronym up) pushing the
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23C3: Analysis of a strong Random Number Generator
Speaker: Thomas Biege This paper (and slides) will descibe the inner workings of the the random number generator (/dev/{u}random) of Linux. Additionally some possible security flaws are shown (entropy overestimation, zero'izing the pool, etc.) For more information visit: http://bit.ly/
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