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Related Concept Videos

Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Wald-Wolfowitz Runs Test II01:17

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Design and Test of an Integrated Random Number Generator with All-Digital Entropy Source.

Luca Crocetti1, Stefano Di Matteo1, Pietro Nannipieri1

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Area of Science:

  • Cybersecurity
  • Cryptography
  • Hardware Security

Background:

  • Random number generation is critical for cryptographic security, impacting key generation, nonces, and initialization vectors.
  • The unpredictability of random sequences directly correlates with security, hindering adversaries from recovering sensitive data.
  • Cryptographically Secure Pseudo-Random Number Generators (CSPRNGs) are essential for producing indistinguishable random outputs.

Purpose of the Study:

  • To design and validate a novel all-digital random number generator (RNG).
  • To meet the rigorous security demands for cryptographic applications as a CSPRNG.
  • To develop a highly portable entropy source with a significant entropy level.

Main Methods:

  • Designed an all-digital RNG incorporating a Deterministic Random Bit Generator (DRBG).
  • Integrated a portable entropy source with high entropy characteristics.
  • Conducted extensive testing using NIST and BSI suites to evaluate entropy and randomness.

Main Results:

  • The designed DRBG meets security requirements for cryptographic applications.
  • The entropy source demonstrated high portability and a high level of entropy.
  • The RNG design passed rigorous NIST and BSI randomness and entropy assessments.

Conclusions:

  • The developed all-digital RNG provides a robust solution for generating cryptographically secure random numbers.
  • The design is validated and ready for integration into advanced hardware platforms like the European Processor Initiative (EPI) chip.
  • This RNG enhances the security of cryptographic operations by ensuring high-quality, unpredictable random number generation.