Automatic Reseeding True Random Number Generator
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Solution Overview
Problem
True Random Number Generators (TRNGs) face challenges such as predictability, inefficiency, and reliability issues due to their reliance on physical noise sources, which can be affected by environmental conditions and manufacturing variations, making them unsuitable for cryptographic systems where predictability and efficiency are crucial.
Innovation Solution
A system and method for reseeding a pseudo random number generator (PRNG) using a TRNG, where a true random number is generated and stored, and used concurrently with the PRNG operation to automatically reseed it, addressing the inefficiencies and reliability concerns by optimizing the seed generation and usage process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a true random number generator uses a physical noise source, then randomness is improved, but reliability deteriorates due to environmental sensitivity
Solution Approach 1:
The patent introduces an intermediary processing system that takes the raw physical noise output and processes it through multiple stages: initial randomization, entropy accumulation in a pool, and post-processing through cryptographic functions. This intermediary layer isolates the final output from environmental variations affecting the physical source, maintaining randomness quality while improving reliability.
Solution Approach 2:
The system dynamically adjusts parameters including the entropy pool refill rate, output generation rate, and reseed timing based on environmental conditions and usage demands. By monitoring the quality of incoming random data and adjusting processing parameters accordingly, the system maintains high randomness quality while compensating for environmental sensitivity.
2Productivity
If a true random number generator operates continuously, then productivity is improved, but reliability deteriorates due to seed exhaustion
Solution Approach 1:
The system pre-accumulates entropy in a seeded pool before it is needed for output generation. By continuously harvesting entropy from the physical noise source and storing it in advance, the system ensures that sufficient seed material is available even during periods of high demand, preventing seed exhaustion while maintaining continuous productivity.
Solution Approach 2:
The entropy pool operates continuously to accumulate random data, and the system implements continuous monitoring and automatic reseeding when thresholds are approached. This ensures uninterrupted random number generation while proactively managing seed availability, preventing exhaustion before it occurs.
3Productivity
If a pseudo random number generator is used instead, then efficiency is improved, but randomness quality deteriorates due to determinism
Solution Approach 1:
The patent merges the strengths of both TRNG and PRNG approaches by using a physical noise source to generate initial entropy (TRNG strength) and then processing this entropy through a deterministic algorithm that produces high-speed output (PRNG strength). The combination achieves both high productivity from the efficient processing and high randomness quality from the physical source, resolving the contradiction between speed and quality.
Data Source
AI summary
A system for reseeding a pseudo random number generator to generate pseudo random numbers includes a true random number generator generating a true random number, a storage device storing the generated true random number, a pseudo random number generator generating pseudo random numbers using the stored true random number as a seed, and a controller coupled to the true random number generator and the pseudo random number generator to (1) generate a new true random number concurrently with the operation of the pseudo random number generator, and storing the new true random number, and (2) reseed the pseudo random number generator with the new true random number.