Hardware Random Number Generator for Unconstrained Cryptographic Key Generation
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Solution Overview
Problem
Hardware Security Modules (HSMs) face challenges in generating large volumes of cryptographic keys due to their deterministic random number generation methods, which are vulnerable and resource-constrained, making them unsuitable for modern high-demand technologies while maintaining security compliance.
Innovation Solution
A computer-implemented method using a hardware random number generator based on statistically random entropy data from natural phenomena to generate cryptographic keys, allowing unconstrained key generation and storage, decoupling the process from HSM resources and enhancing security by reducing determinism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If HSMs use deterministic random number generators for key generation, then compliance with security standards is maintained, but the rate of key generation is constrained and security vulnerability increases
Solution Approach 1:
The system separates the key generation process into two independent components: a hardware random number generator that produces high-entropy numerical data, and the HSM that stores and manages cryptographic keys. This segmentation allows the RNG to operate at high speed while the HSM maintains security compliance, resolving the contradiction between key generation rate and security compliance.
Solution Approach 2:
The hardware random number generator acts as an intermediary between natural phenomena (entropy sources) and the HSM. It converts physical randomness into numerical data that feeds key generation, enabling the HSM to produce compliant keys at higher rates without directly handling the randomness generation process.
2Reliability
If HSMs use deterministic random number generators, then device security and compliance are maintained, but key generation volume is limited
Solution Approach 1:
The system performs preliminary random number generation and key creation in advance using the hardware RNG, then stores these pre-generated keys in the HSM. This allows the HSM to quickly provision keys during high-demand periods without being constrained by its own slow random number generation capabilities, thereby increasing key generation volume while maintaining security.
3Reliability
If HSM resources are used for key generation, then compliance is maintained, but the process is constrained by processor and memory limitations
Solution Approach 1:
The system extracts the computationally intensive random number generation function from the HSM and places it in an external hardware RNG. This removes the processor and memory constraints from the key generation process while the HSM retains its security compliance functions, effectively decoupling computational complexity from security-critical operations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the generation and storage of cryptographic keys at higher rates and volumes, improving security and compliance by leveraging truly random number generation, reducing reliance on HSM resources and ensuring key availability even during high demand periods.
Implementation Method 1
quantum phenomena such as photoelectric effects
Data Source
AI summary
A computer implemented method of generating cryptographic keys for a hardware security module (HSM), the method including generating a plurality of cryptographic keys and storing the cryptographic keys for use by the HSM in providing cryptography functions, wherein the cryptographic keys are generated based on numerical data generated by a hardware random number generator, such that a rate of generation of the cryptographic keys unconstrained by the resources of the HSM, wherein the hardware random number generator operates based on a plurality of statistically random entropy data sources originating from natural phenomena so as to increase a degree of randomness of the numerical data.