True Random Number Generation via Physical Event Aggregation
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Solution Overview
Problem
Current computer systems face challenges in generating truly random numbers, as pseudorandom number generators are deterministic and vulnerable to attacks, while true random number generators using physical events introduce complexities and vulnerabilities.
Innovation Solution
The system utilizes a combination of data capture devices such as cameras, microphones, GPS, accelerometers, and biometric sensors to capture and aggregate physical event data, which is then processed to produce a random value for hash generation and security applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pseudorandom number generators are used, then computational efficiency is improved, but security reliability deteriorates due to determinism and predictability
Solution Approach 1:
The system segments the random number generation function by separating the deterministic computational operations from the true random physical measurement operations. The true random number generator handles only the random number generation, while separate cryptographic functions handle the security applications, allowing each component to be optimized independently for its specific function.
Solution Approach 2:
The patent introduces a true random number generator as an intermediary component that bridges the gap between physical random events and cryptographic applications. This intermediary provides true randomness to the system without requiring the entire system to be rewritten, allowing efficient computational operations to proceed while maintaining security through the insertion of genuine random elements.
2Reliability
If true random number generators using physical events are used, then security reliability is improved, but device complexity increases due to additional hardware and monitoring systems
Solution Approach 1:
The system uses software-based simulation of physical random events rather than direct hardware connections to physical phenomena. The true random number generator creates virtual representations of random physical processes through algorithmic models, eliminating the need for complex hardware monitoring systems while maintaining the essential randomness required for security applications.
Solution Approach 2:
The patent replaces mechanical or physical hardware systems with software-based implementations. Instead of using physical random number generators that require hardware monitoring and physical event capture, the system uses software algorithms that simulate random physical processes, significantly reducing device complexity while maintaining security reliability.
3Reliability
If physical event observation is used for random number generation, then true randomness is improved, but measurement precision deteriorates due to bucketing and granularity effects
Solution Approach 1:
The system changes the parameters of measurement by operating at a finer resolution than physical event bucketing allows. The true random number generator processes physical events at the individual event level rather than aggregating them into buckets, maintaining true randomness while improving measurement precision through high-resolution capture and processing of individual random events.
Data Source
AI summary
Aspects of the disclosure are directed to solutions for generating a distinctive value in a computing device. A captured data gathering module is to interface with the plurality of data capture devices and to read data output from each of them. The data output has a randomness characteristic. A captured data aggregation module is to combine the data output from at least two different data capture devices to produce an aggregated output. A transformation module is to compute a transformation of the aggregated output to produce a distinctive value that is based on the randomness characteristic.