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Related Concept Videos

Entropy02:39

Entropy

Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Entropy01:18

Entropy

The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

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Machine Learning Predictors for Min-Entropy Estimation.

Javier Blanco-Romero1, Vicente Lorenzo1,2, Florina Almenares Mendoza1

  • 1Department of Telematic Engineering, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain.

Entropy (Basel, Switzerland)
|February 26, 2025
PubMed
Summary

Machine learning models can better estimate average min-entropy for random number generators (RNGs) than traditional methods. This is crucial for enhancing cybersecurity in cryptographic applications.

Keywords:
autoregressive processesgeneralized binary autoregressive modelsmachine learning predictorsmin-entropy estimationrandom number generators

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Area of Science:

  • Cryptography
  • Machine Learning
  • Information Theory

Background:

  • Accurate entropy estimation is vital for the security of random number generators (RNGs) used in cryptography.
  • Traditional methods for assessing min-entropy may not fully capture the nuances required for modern cybersecurity applications.

Purpose of the Study:

  • To investigate the efficacy of machine learning predictors for estimating min-entropy in RNGs.
  • To compare the performance of machine learning models against traditional predictors like NIST SP 800-90B.
  • To analyze the relationship between average min-entropy and traditional min-entropy based on the number of target bits.

Main Methods:

  • Utilized machine learning models, including hybrid CNN-LSTM and GPT-2 architectures.
  • Employed data from generalized binary autoregressive models, a class of Markov processes.
  • Evaluated predictors based on their estimation of average min-entropy and its correlation with traditional min-entropy.

Main Results:

  • Machine learning predictors, particularly those leveraging sequence correlations, primarily estimate average min-entropy.
  • The performance of predictors is dependent on the number of target bits being predicted.
  • Machine learning models demonstrated superior performance compared to NIST SP 800-90B predictors in specific scenarios.

Conclusions:

  • The number of target bits is a critical factor in min-entropy assessment for RNGs.
  • Machine learning offers a promising avenue for advancing entropy estimation techniques.
  • Enhanced entropy estimation through machine learning can significantly bolster cryptographic security.