Multi-factor Entropy Pool Sizing for Random Number Generators
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Solution Overview
Problem
Existing random number generators in computer systems are vulnerable to prediction by malicious parties due to insufficient multi-factor entropy sourcing, leading to pseudo-random number generation and compromised security.
Innovation Solution
Implementing a multi-factor entropy sourcing method where the entropy pool management component collects and mixes random bits from multiple physical devices, ensuring each bit is derived from multiple sources, thereby increasing the entropy pool size and reducing predictability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random bits are collected from multiple entropy sources and mixed into the entropy pool, then the entropy pool size and security are improved, but the system complexity and computational overhead increase
Solution Approach 1:
The system divides entropy collection into separate entropy sources (physical devices, system events, hardware components) that can be independently monitored and combined. Each source contributes random bits to the entropy pool through modular collection processes, allowing the system to manage complexity by treating each source as an independent segment rather than a monolithic system.
Solution Approach 2:
The patent combines multiple entropy sources by mixing random bits from various physical devices and system events into a single entropy pool. This merging process uses cryptographic mixing functions to combine entropy from diverse sources (network activity, disk I/O, CPU timing, hardware random generators) into a unified pool that maintains the security properties of all contributing sources while simplifying the overall structure.
2Reliability
If entropy is conservatively estimated by requiring multiple bits from different entropy sources per pool bit, then the security against prediction is improved, but the entropy pool filling speed decreases
Solution Approach 1:
The system dynamically adjusts the parameter of how many entropy source bits are required to fill one entropy pool bit based on the current state of the system. When entropy sources are abundant and diverse, the conservative ratio can be relaxed to allow faster pool filling. When sources are limited or potentially compromised, the ratio increases to maintain security. This parameter adjustment resolves the contradiction by making the system adaptive rather than static.
Solution Approach 2:
The patent implements a tiered approach where different levels of entropy collection strictness are applied. In some cases, full conservative estimation is applied for maximum security, while in other cases where multiple diverse sources are available, the system can accept partial entropy contribution from additional sources to accelerate pool filling without significantly compromising security. This partial application of the conservative principle allows flexibility in balancing speed and security.
Data Source
AI summary
Systems and methods for multi-factor entropy sourcing for random number generators. An example method may comprise: identifying, by a processing device, a plurality of entropy sources; receiving random bits from each of the plurality of entropy sources; identifying a minimum number of bits among numbers of bits received from each of two or more entropy sources of the plurality of entropy sources; mixing, into an entropy pool, at least the identified minimum number of bits received from each entropy source of two or more entropy sources; and increasing a size of the entropy pool by the identified minimum number of bits.