Permissioned Blockchain for Secure Training Data Consensus
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Solution Overview
Problem
In health and safety-critical applications like medical diagnosis, there is a need to share and validate training examples securely and efficiently across organizations while ensuring authenticity and correctness, as a single organization cannot generate enough validated examples on its own.
Innovation Solution
A permissioned blockchain network is formed to distribute and validate training examples among endorsing peers, where automated analysis is performed to determine consensus on inferences, and only when consensus is reached, the examples are committed to the blockchain, ensuring secure and controlled sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If training examples are shared across multiple organizations, then the quantity and diversity of training data increases, but the security and authenticity control becomes more difficult
Solution Approach 1:
The system segments the training example validation process into multiple independent endorsing peers, each performing automated analysis and voting. This segmentation allows the system to handle large quantities of training examples from multiple organizations while maintaining authenticity control through distributed consensus rather than centralized validation.
Solution Approach 2:
The patent introduces an intermediary blockchain network that mediates between multiple organizations sharing training examples. The blockchain acts as a trusted intermediary that records and verifies the authenticity of training examples through consensus mechanisms, enabling secure sharing across organizations without requiring direct trust between them.
2Reliability
If automated analysis is performed by multiple endorsing peers, then the reliability of inference increases through consensus, but the complexity of the system increases
Solution Approach 1:
The endorsing peers in the blockchain network perform multiple functions: they validate training examples, perform automated analysis, cast votes on inferences, and maintain the distributed ledger. This multi-functionality reduces the need for separate specialized systems for each function, thereby managing complexity while achieving reliable consensus through a unified distributed architecture.
3Reliability
If training examples are validated through consensus before committing to blockchain, then the authenticity is improved, but the time required for validation increases
Solution Approach 1:
The system performs preliminary automated analysis and validation of training examples by multiple endorsing peers before the examples are committed to the blockchain. This preliminary action ensures authenticity is established in advance through consensus, allowing the blockchain commitment itself to be a relatively quick finalization step rather than a time-consuming process.
Data Source
AI summary
An example operation may include one or more of receiving an example in a blockchain network, distributing the example to a plurality of endorsing peers of the blockchain network, performing, by one or more of the endorsing peers, automated analysis of the example to determine an inference for the example, determining if there is a consensus of inference amongst the plurality of endorsing peers, and committing the example to a blockchain of the blockchain network when there is a consensus of inference.