Cosmic Ray Data Preprocessing for Hardware-Free Randomness Generation
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Solution Overview
Problem
Existing hardware-based True Random Number Generators (TRNGs) face challenges such as bias, speed, reliability, cost, and scalability, limiting their effectiveness in generating true randomness.
Innovation Solution
A system that preprocesses and conditions raw cosmic ray data using statistical methods and a Generative Adversarial Network (GAN) to generate high-quality random sequences, eliminating the need for specialized hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware-based True Random Number Generators (TRNGs) are used to generate true randomness, then randomness quality is improved, but cost and device complexity increase
Solution Approach 1:
The patent replaces hardware-based TRNG systems with a software-based solution using GANs. The generator network learns to produce random sequences that are statistically indistinguishable from true random data, eliminating the need for specialized hardware while maintaining randomness quality. The discriminator network verifies the randomness of generated sequences, ensuring reliability without additional physical components.
Solution Approach 2:
The GAN framework creates synthetic random data that copies the statistical properties of true random data. The generator learns to replicate the entropy and distribution characteristics of cosmic ray data or other random sources, producing convincing fakes that pass randomness tests without requiring the original physical random number generator hardware.
2Reliability
If hardware-based TRNGs are deployed, then true randomness is achieved, but scalability and ease of operation deteriorate
Solution Approach 1:
The trained GAN model serves as a universal random number generator that can be deployed across multiple systems and applications. Once trained on sufficient random data, the generator can produce random sequences for cryptography, simulations, secure communications, and other applications without requiring separate hardware TRNGs for each use case, greatly improving scalability.
Solution Approach 2:
Replacing physical TRNG hardware with software-based GAN generation enables easy replication and distribution of random number generation capability across distributed systems, cloud environments, and various platforms without deploying additional hardware at each location.
3Measurement precision
If statistical filtering methods are applied to cosmic ray data, then data quality is improved, but processing time increases
Solution Approach 1:
The patent applies statistical filtering methods (Theil-Sen estimator, Hodges-Lehmann estimator, Modified Z-score) and outlier removal techniques to preprocess cosmic ray data before feeding it to the GAN. This preliminary cleaning of the training data improves the quality of randomness learned by the model, ensuring that the generator is trained on high-quality random patterns without artifacts or biases from poor data.
Data Source
AI summary
The present disclosure describes a method for conditioning and preprocessing raw cosmic ray data to extract features and generate a binary string representing the inherent randomness in the data. Measurable characteristics of cosmic ray particles, such as arrival times and amplitudes, are processed to create random binary sequences. These binary strings are then used to train a generative adversarial network (GAN) framework. In the GAN framework, the generator creates random sequences resembling the target distribution, which matches the entropy of the conditioned cosmic data. The discriminator evaluates the generated sequences by comparing them to the conditioned cosmic data and assigns a randomness score that characterizes the quality of the generated sequences. This adversarial process ensures the generation of high-quality random sequences that are statistically indistinguishable from the conditioned input data.