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Modular entropy multiplication

As explained in Hacking Cryptography

A technique for constructing hardware random number generators by combining entropy sources with modular arithmetic and a physical model for monitoring output health.

Modular entropy multiplication (MEM) constructs a hardware random number generator from analog electrical noise. It amplifies the noise, drives large voltage fluctuations through simple rules, and supplies a physical model for monitoring output health. section 2.2.1

MEM’s engineering role is to combine entropy generation with ongoing health assessment:

  • Accepts an analog noise source, potentially produced by electrical-noise methods such as avalanche or Zener effects and ring oscillators. section 2.2.1
  • Amplifies the source noise and repeatedly drives the voltage between dramatic fluctuations according to simple rules. section 2.2.1
  • Uses a physical model to assess RNG health, addressing a limitation of avalanche- and Zener-diode constructions that lack a good health-assessment mechanism. section 2.2.1 section 2.3
  • Serves as a newer TRNG construction intended to resist attacks aimed at other electrical-noise-based RNGs while remaining low-cost and resistant to electromagnetic interference. section 2.2.1
Figure 2.4 TRNGs sample physical phenomena to generate random numbers.
figure

Use the figure to place MEM within the broader TRNG pattern of sampling physical phenomena for unpredictable output. section 2.2.1

How Hacking Cryptography covers it

How deep it goes
Defines the noise-amplification and voltage-fluctuation principle but stops short of circuit schematics, equations, or implementation steps.
Examples
Uses amplified electrical noise and dramatic voltage fluctuations as its concrete construction examples.
What you can do after
Evaluate TRNG designs by considering both entropy generation and the availability of a physical health-monitoring model.
How it connects
Connects TRNGs with analog noise, avalanche and Zener diodes, ring oscillators, electromagnetic interference, and CSPRNG seeding.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Hacking Cryptography. The section links point to the parts of the book it draws on.