Post-Processing Apparatus for Non-Memory-Less Noise Source Random Bit Sequences
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Solution Overview
Problem
Conventional methods for post-processing random bit sequences from noise sources, especially those that are not memory-less, fail to ensure independence and sufficient entropy of generated random words, which is critical for secure applications.
Innovation Solution
The apparatus and method involve a noise source modeled as a discrete time homogeneous Markov chain, a feedback shift register, and a multiplexer with a controller that skips sequences of consecutive bits and derives random words from intervening sub-sequences, ensuring independence and improved statistical quality through a discrete time homogeneous Markov chain model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If conventional compression algorithms are used on raw bit sequences from non-memory-less noise sources, then the output random words are compressed, but the independence of the generated random words cannot be proven
Solution Approach 1:
The raw bit sequence is divided into multiple sub-sequences, where each sub-sequence is processed independently to generate a random word. The skipping means selectively chooses which sub-sequences to process, ensuring that only sufficiently independent sub-sequences are used for random word generation, thus maintaining both compression efficiency and statistical independence.
Solution Approach 2:
The skipping means performs preliminary filtering of the raw bit sequence before the deriving means processes it. By pre-identifying and skipping insufficient sub-sequences, the system ensures that only high-quality, independent sub-sequences reach the derivation stage, guaranteeing the independence of generated random words while maintaining compression.
2Productivity
If sub-sequences from non-memory-less noise sources are processed consecutively, then the generation rate is high, but the independence of output random words is compromised
Solution Approach 1:
The system dynamically adjusts the skipping behavior based on the measured independence quality of each sub-sequence. The skipping means evaluates sub-sequences in real-time and adaptively decides whether to process or skip each one, allowing the system to maintain high generation rates when quality is sufficient while ensuring independence when quality degrades.
Solution Approach 2:
The system changes the processing parameters (specifically, the decision to skip or process a sub-sequence) based on the statistical properties of the noise source. By monitoring independence metrics and adjusting the skipping strategy accordingly, the system optimizes both generation rate and independence guarantee.
3Reliability
If feedback strategies are employed to improve robustness against variations, then the noise source robustness is improved, but the noise source becomes non-memory-less requiring more complex post-processing
Solution Approach 1:
The system employs a feedback mechanism where the independence quality of generated random words is continuously monitored and fed back to the skipping means. This feedback loop allows the post-processing device to adapt its skipping strategy in real-time, managing the complexity introduced by non-memory-less noise sources while maintaining robustness and independence guarantees.
Data Source
AI summary
An apparatus for post processing a raw bit sequence generated by a noise source, comprises a derivation unit for deriving a random word from each bit sequence input into the derivation unit and a skip unit for skipping skip sequences of consecutive bits of the raw bit sequence and inputting intervening sub-sequences of bits of the raw bit sequence between the skip sequences into the derivation unit.