Genetic Information Random Number Generation
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Solution Overview
Problem
Existing random number generators, particularly pseudo-random number generators, fail to produce truly unpredictable numbers, which are essential for various technical applications, and they often require careful calibration and controlled environments to maintain statistical unpredictability, raising security concerns due to the lack of auditable and verifiable true randomness sources.
Innovation Solution
A method utilizing genetic information from biological organisms, where a pseudo-random number generator is seeded with an entropy source to select positions in DNA or RNA sequences, encoding these values into bit pairs to generate truly random numbers, which can be verified and validated, providing a potentially limitless source of randomness without the need for controlled environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pseudo-random number generators are used, then random numbers can be generated computationally, but the output is not truly unpredictable and fails to meet security requirements
Solution Approach 1:
The patent replaces hardware-based physical noise sources with a software-based system that uses genetic information sequences as an entropy source. This substitution eliminates the need for controlled physical environments and hardware calibration while providing truly unpredictable random numbers through the inherent randomness of genetic sequences combined with cryptographic processing
Solution Approach 2:
The patent introduces genetic information sequences as an intermediary between the physical world and the computational system. These sequences serve as a verifiable entropy source that bridges the gap between physical randomness and computational processing, enabling both true randomness and auditability without requiring direct hardware noise sources
2Reliability
If hardware random number generators using physical noise sources are used, then truly random numbers can be produced, but the system requires careful calibration and controlled environments to maintain statistical unpredictability
Solution Approach 1:
The patent replaces complex hardware noise generation systems with a software-based genetic sequence processing system. This eliminates the need for controlled physical environments, hardware calibration, and specialized operational procedures while maintaining true randomness through the inherent unpredictability of genetic information
Solution Approach 2:
The system uses readily available genetic information sequences as self-contained entropy sources that do not require external calibration or controlled environments. The genetic sequences inherently provide the randomness needed, eliminating the need for ongoing maintenance and environmental control
3Reliability
If hardware random number generators are used, then random numbers can be generated, but the process is not auditable or validatable and security trust is eroded
Solution Approach 1:
The patent implements a feedback mechanism where the genetic sequence, selection process, and generated random numbers are linked through verifiable computation. This allows the generation process to be audited and validated by re-running the same genetic sequence through the selection algorithm, providing cryptographic proof of randomness without revealing the entropy source itself
Solution Approach 2:
The patent performs preliminary actions by securely storing the genetic sequence and selection parameters before random number generation. This enables future audit and validation by preserving the exact state used for generation, allowing verification without compromising the security or revealing the entropy source
4Productivity
If pseudo-random number generators are used, then random numbers can be generated without physical sources, but the numbers do not conform to true randomness requirements for cryptographic applications
Solution Approach 1:
The patent merges the high-speed computational capabilities of pseudo-random number generators with the true entropy of genetic information sequences. By combining cryptographic processing with biological entropy sources, the system achieves both fast generation speeds and cryptographic security strength
Solution Approach 2:
The system creates a composite approach by combining software-based cryptographic processing with biological entropy sources. This composite method leverages the speed of computation and the true randomness of genetic information to produce cryptographically secure random numbers at high speed
Data Source
AI summary
The disclosure provides computer-implemented methods, computing apparatuses and computer program products for generating a random number based on genetic information from a biological data source containing at least the genetic information sequenced from a biological organism. In response to receiving a request for a random number at the computing device, a seed value is obtained from an entropy source accessible by the computing device and used to initialize a pseudo random number generator. A sequence of values derived from genetic information for a biological organism is retrieved from a biological data source from which values are read in selected positions in the sequence of values derived from genetic information. The values are encoded to pairs of bits using an encoding scheme and assembled to provide a bit string as a random number. At least one of the selections is based on the pseudo random output.