Look up words and phrases in comprehensive, reliable bilingual dictionaries and search through billions of online translations.Date: October 26, 2021 Source: Nagoya University Summary: A team of physicists has demonstrated numerically a dynamical one-parameter scaling called.Download Intel® Digital Random Number Generator (DRNG) Software Implementation Guide Entropy is a measure of the number of ways a thermodynamic system can be arranged, commonly described as the 'disorder' of a system. Translate texts with the worlds best machine translation technology, developed by the creators of Linguee. Suggest as a translation of 'entropy source' Copy DeepL Translator Linguee.
This section describes the nature of an RNG and its pseudo- (PRNG) and true- (TRNG) implementation variants, including modern cascade construction RNGs. This document includes the following sections:Section 2: Random Number Generator (RNG) Basics and Introduction to the DRNG. Among other things, the DRNG using the RDRAND instruction is useful for generating high-quality keys for cryptographic protocols, and the RSEED instruction is provided for seeding software-based pseudorandom number generators (PRNGs)This Digital Random Number Generator Software Implementation Guide is intended to provide a complete source of technical information on RDRAND usage, including code examples. IntroductionIntel® Secure Key, code-named Bull Mountain Technology, is the Intel name for the Intel® 64 and IA-32 Architectures instructions RDRAND and RDSEED and the underlying Digital Random Number Generator (DRNG) hardware implementation.
Entropy Source Verification And Suggestions
This includes platform support verification and suggestions on DRNG-based libraries.Programmers who already understand the nature of RNGs may refer directly to section 4 for instruction references and code examples. This section provides reference information on the RDRAND and RDSEED instructions and code examples showing its use. In this section, we provide a technical overview of the DRNG, including its component architecture, robustness features, manner of access, performance, and power requirements.Section 4: RDRAND and RDSEED Instruction Usage.
To understand how it differs from existing RNG solutions, this section details some of the basic concepts underlying random number generation. RNG Basics and Introduction to the DRNGThe Digital Random Number Generator, using the RDRAND instruction, is an innovative hardware approach to high-quality, high-performance entropy and random number generation. Nearly all developers will want to look at section 3, which provides a technical overview of the DRNG.
The RNG is fast in returning a value (i.e., low response time) and can service a large number of requests within a short time interval (i.e., highly scalable). External entropy sources like the time between a user's keystrokes or mouse movements may likewise, upon further analysis, show that values do not distribute evenly across the space of all possible values some values are more likely to occur than others, and certain values almost never occur in practice.Beyond these requirements, some other desirable RNG properties include: Sampling the seconds value from the system clock, a common approach, may seem random enough, but process scheduling and other system effects may result in some values occurring far more frequently than others.
Thus, while a generated sequence of values exhibit the statistical properties of randomness (independence, uniform distribution), overall behavior of the PRNG is entirely predictable.In some contexts, the deterministic nature of PRNGs is an advantage. This is because a PRNG is computing the next value based upon a specific internal state and a specific, well-defined algorithm. That is, given a particular seed value, the same PRNG will always produce the exact same sequence of "random" numbers.
After a surprisingly small number of observations (e.g., 624 for Mersenne Twister MT19937), each and every subsequent value can be predicted. Even with a sophisticated and unknown seeding algorithm, an attacker who knows (or can guess) the PRNG in use can deduce the state of the PRNG by observing the sequence of output values. An attacker who knew the PRNG in use and also knew the seed value (or the algorithm used to obtain a seed value) would quickly be able to predict each and every key (random number) as it is generated. Consider a server application that generates random numbers to be used as cryptographic keys in data exchanges with client applications over secure communication channels. PRNGs provide a way to generate a long sequence of random data inputs that are repeatable by using the same PRNG, seeded with the same value.In other contexts, however, this determinism is highly undesirable.
Even more common, attackers may discover or infer PRNG seeding by narrowing its range of possible values or snooping memory in some manner. Furthermore, an attacker could discover any deterministic algorithm by various means (e.g., disassemblers, sophisticated memory attacks, a disgruntled employee). Such approaches improve the problem of inferring a PRNG and its state by greatly increasing its computational complexity, but the resulting values may or may not exhibit the correct statistical properties (i.e., independence, uniform distribution) needed for a robust random number generator. Various techniques have been invented in this domain, for example, applying a cryptographic hash to a sequence of consecutive integers, using a block cipher to encrypt a sequence of consecutive integers ("counter mode"), and XORing a stream of PRNG-generated numbers with plaintext ("stream cipher").
This poses a serious problem for many TRNGs because sampling an entropy source external to the computing system typically requires device I/O and long delay times relative to the processing speeds of today's computer systems.
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