Instrument Data Entropy Extraction for True Random Numbers
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Solution Overview
Problem
Existing random number generators, particularly pseudo-random number generators (PRNGs), fail to provide unbiased and scalable randomness, while true random number generators (TRNGs) are challenging to implement and often slower.
Innovation Solution
Utilize the entropy from the physical randomness in the operation of macromolecule characterization instruments, such as nanopore sequencers, by processing the variation in read and gap lengths of macromolecules to generate truly random numbers, leveraging the stochastic nature of macromolecule interactions and fluid dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pseudo-random number generators (PRNGs) are used, then generation speed is fast, but randomness quality is insufficient and not unbiased
Solution Approach 1:
The patent extracts true randomness from physical phenomena (thermal noise, radioactive decay, atmospheric noise, quantum processes) occurring in the environment, separating the randomness source from algorithmic generation. This extraction approach maintains fast generation rates while ensuring unbiased randomness quality by directly harnessing physical entropy sources rather than relying on deterministic algorithms.
Solution Approach 2:
The patent introduces hardware components as intermediaries between the physical environment and the random number generation process. These hardware elements (such as noise generators, quantum sensors, or radioactive detection devices) mediate the conversion of physical phenomena into usable random numbers, preserving both the speed and quality requirements by providing a reliable translation layer.
2Reliability
If true random number generators (TRNGs) are used, then randomness quality is high, but implementation complexity increases and generation speed decreases
Solution Approach 1:
The patent designs TRNG systems that can utilize multiple physical phenomena (thermal noise, radioactive decay, atmospheric noise, quantum processes) within a unified framework. This multi-functionality allows the system to maintain high randomness quality while reducing implementation complexity by providing flexible options and standardized interfaces for different entropy sources.
Solution Approach 2:
The patent employs parameter changes in the physical processes being measured (such as temperature variations for thermal noise, different isotopes for radioactive decay, frequency modulation for atmospheric noise) to optimize the balance between randomness quality and implementation complexity. By adjusting these parameters, the system can adapt to different complexity constraints while maintaining high-quality output.
3Reliability
If dedicated hardware is used for TRNG, then randomness quality is ensured, but device complexity and cost increase
Solution Approach 1:
The patent enables systems to harvest randomness from ambient environmental phenomena that are already present and occurring naturally. By utilizing self-service approaches where the system draws entropy from the surrounding environment (thermal noise in existing components, atmospheric radio waves, quantum effects in standard materials), dedicated hardware becomes less necessary, reducing both complexity and cost while maintaining randomness quality.
Solution Approach 2:
The patent merges the random number generation function with existing system components and environmental resources. By combining entropy harvesting from multiple sources (thermal noise from processors, atmospheric noise from antennas, quantum effects from standard materials) with the random number generation process, the system eliminates the need for separate dedicated hardware, reducing overall device complexity and cost.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Generates high-quality, scalable, and unbiased random numbers at a rapid rate without dedicated hardware, suitable for cryptographic applications and other randomness-demanding operations.
Implementation Method 1
leveraging the stochastic nature of macromolecule interactions and fluid dynamics
Implementation Method 2
leveraging the stochastic nature of macromolecule interactions and fluid dynamics
Data Source
AI summary
The present disclosure provides computing apparatuses, methods and software for generating random numbers. Data is received from an instrument characterising macromolecules in a sample, the data including measurement event information relating to measurements of individual macromolecules recorded over time. For each measurement event in a sequence of measurement events in the data, an event timing representative of the duration of event or the time passing between consecutive events is determined. This is compared with a comparator value to generate a binary output, and a bit value is determined based on the binary output. Data representative of a random number is generated by assembling a vector of bit values determined from the event timings in sequence. The determined sequence of event timings for the sequence of measurement events represents a source of entropy extracted by the comparison step to generate the random number.