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Batch and stream entropy with fixed partitions for chaos-based random bit generators.

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This study introduces reliable methods for estimating entropy from chaotic physical systems, ensuring security against guessing attacks. The proposed measures are adaptable to both batch and streaming data transfer modes.

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Area of Science:

  • Information Theory
  • Chaos Theory
  • Cryptography

Background:

  • Entropy estimation is crucial for secure random number generation.
  • Chaotic physical systems offer a promising source for high-quality entropy.
  • Existing methods may be vulnerable to sophisticated attacks and data transfer limitations.

Purpose of the Study:

  • To develop reliable entropy estimation measures for chaotic physical systems.
  • To ensure security against guessing attacks in entropy transfer.
  • To address practical considerations of digital sampling and data transfer modes.

Main Methods:

  • Proposed novel measures for entropy estimation from chaotic systems.
  • Analyzed measures' reliability against guessing attacks.
  • Investigated the impact of fixed partitions for digital signal sampling.
  • Developed distinct measures for batch and streaming entropy transfer.

Main Results:

  • The proposed measures provide reliable entropy estimation.
  • Security is enhanced against guessing attacks.
  • Fixed partitions are shown to align with practical digital sampling.
  • Batch and stream entropy estimations exhibit distinct dependencies on chaos type and partitions.

Conclusions:

  • Reliable entropy estimation from chaotic systems is achievable.
  • The proposed measures offer robust security and flexibility for different data transfer scenarios.
  • Understanding the influence of partitions is key for optimizing entropy extraction.