Pseudo Random Number Generation Array for Cryptographic Security
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Many Pseudo Random Number Generators (PRNGs) produce sequences that exhibit bias or identifiable patterns, which can introduce vulnerabilities in cryptographic applications and skew simulation results, making them computationally distinguishable from True Random Number sequences.
Innovation Solution
A PRNG Array with a multiplicity of independent, possibly heterogeneous PRNGs of different types and variations, where a Selector PRNG computes and scales an index to randomly select which PRNG generates the requested pseudo-random number, introducing output sequence discontinuities that disrupt bias and obfuscate patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single PRNG type is used, then the device complexity is low, but the output sequence exhibits bias and identifiable patterns that can be detected and exploited
Solution Approach 1:
The patent divides the PRNG system into multiple independent PRNG instances (e.g., LFSR1, LFSR2, LFSR3) with different feedback polynomials and initial states. Each PRNG instance generates a separate pseudo-random sequence, and the final output is formed by combining these sequences. This segmentation prevents any single PRNG from exhibiting detectable patterns, as each contributes differently to the overall output stream.
Solution Approach 2:
The patent combines multiple PRNG output sequences into a single unified output stream. The combination is achieved through bitwise XOR operations or other mixing functions that blend the sequences from multiple PRNG instances. This merging process ensures that patterns in individual sequences are obscured by the combined output, achieving computational indistinguishability from true random numbers.
2Reliability
If multiple PRNG types are used, then the output sequence becomes computationally indistinguishable from True Random Number sequences, but the device complexity increases
Solution Approach 1:
The patent assigns different characteristics to each PRNG instance, such as different feedback polynomials, different initial states, and different update rates. This local differentiation ensures that each PRNG contributes unique properties to the output stream, making it difficult for attackers to identify patterns or predict future values based on analysis of individual sequence characteristics.
Solution Approach 2:
The patent implements dynamic switching between different PRNG instances based on a selector mechanism. The selector can change which PRNG instance is active at any given time, introducing variability into the output stream. This dynamic approach prevents statistical analysis from focusing on a single PRNG's patterns, as the active PRNG changes over time.
3Ease of manufacture
If a fixed PRNG configuration is used, then the implementation is simple, but the output exhibits periodicity that can be exploited in cryptographic attacks
Solution Approach 1:
The patent uses periodic switching between different PRNG instances, where the selector cycles through available PRNGs in a predetermined pattern. This periodic action ensures that no single PRNG operates continuously, preventing the emergence of long-term periodicity. The periodic switching pattern itself is kept secret or sufficiently complex that it does not create detectable patterns in the output stream.
Data Source
AI summary
The disclosure is directed to a multiplicity-dimensioned array of pseudo random number generation means, each array member using an independent selector pseudo random number generator means to generate a pseudo random number value used to identify a second pseudo random number generator means within a multiplicity of pseudo random number generator means, the second pseudo random number generator means producing a value that is returned to a pseudo random number generator requestor unchanged.