Randomness in Distributed Networks via Consensus-Driven Seed Generation
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Solution Overview
Problem
Distributed networks providing distributed computing services face challenges in securely and efficiently providing randomness to the nodes of a replicated computing cluster.
Innovation Solution
A distributed network with a replicated computing cluster that performs consecutive consensus rounds and processing rounds, where each payload is assigned a random seed computed after consensus is reached, using a threshold-signature protocol for secure randomness generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If randomness is provided before consensus is reached, then processing efficiency is improved, but security against adversarial bias is worsened
Solution Approach 1:
The system performs preliminary actions (consensus reaching) before the critical action (randomness provision) to ensure security. By requiring consensus to be established before randomness is made available, the system prevents adversarial bias while maintaining a structured processing flow where subsequent operations can efficiently utilize the predetermined randomness.
Solution Approach 2:
The patent inverts the conventional approach by not providing randomness first and then reaching consensus, but rather reaching consensus first and then providing randomness. This inversion ensures that the randomness cannot be biased by adversaries who would otherwise have the opportunity to influence the random value generation before consensus is established.
2Reliability
If randomness computation is performed after consensus, then security is improved, but processing time is increased
Solution Approach 1:
The consensus process serves as a preliminary action that establishes the foundation for secure randomness provision. By completing consensus first, the system ensures security is not compromised, and the subsequent randomness computation can proceed without re-evaluating consensus conditions, minimizing additional time loss.
Solution Approach 2:
The system uses the consensus state itself to drive the randomness generation process. The agreed-upon state from consensus automatically becomes the input for randomness computation, eliminating the need for separate coordination or validation steps and reducing overall processing time while maintaining security.
3Reliability
If random seed is computed for each payload after consensus, then randomness security is improved, but computational overhead is increased
Solution Approach 1:
The system applies local quality by generating randomness specifically tailored to each payload's context. Each payload receives a dedicated random seed computed from the consensus state and payload-specific inputs, ensuring that randomness is securely bound to each individual processing unit without requiring global re-computation for all payloads.
Solution Approach 2:
The randomness generation leverages parameter changes in the consensus state as payloads are processed. By using the evolving consensus state combined with payload-specific parameters, the system generates unique random seeds for each payload without requiring redundant computation, as each payload's parameters naturally differ and drive distinct randomness generation.
Data Source
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AI summary
Embodiments of the invention relate to a distributed network which comprises a replicated computing cluster. The replicated computing cluster comprises a plurality of nodes, wherein each of the plurality of nodes of the replicated computing cluster is configured to run a replica and each of the replicas is configured to run one or more computational units. The replicated computing cluster is configured to perform consecutive consensus rounds to reach a consensus on a sequence of payloads and to perform consecutive processing rounds comprising a consecutive processing of the sequence of payloads in a deterministic and replicated manner. The replicated computing cluster is further configured to perform consecutive computations of a random seed for each of the payloads of the sequence of payloads and to use the random seed of a respective payload of the sequence of payloads to provide randomness to the payload. The respective computation of the random seed for a respective payload is performed only after a consensus on the respective payload has been reached.