Mining Node Measurement Dataset Validation in Distributed Databases
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Solution Overview
Problem
The integrity of measurement datasets in distributed databases, such as Blockchain, can be compromised due to malfunctioning oracles or fraud, which affects the validity and integrity of data stored and relied upon for various functionalities.
Innovation Solution
A method is implemented at a mining node of a distributed database infrastructure that includes validating measurement datasets by comparing them with a reference dataset, using a consensus mechanism within the Blockchain infrastructure to increase trust levels and prevent manipulation, where the measurement dataset can be digitally signed close to its origin to ensure authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If measurement datasets are stored in a distributed database without validation, then data storage efficiency is improved, but data integrity and reliability deteriorate due to potential oracle malfunction or fraud
Solution Approach 1:
The system performs preliminary validation of measurement datasets by comparing oracle-provided data against pre-stored reference datasets before accepting the data into the distributed database. This advance verification prevents unreliable data from compromising database integrity while maintaining efficient storage operations.
Solution Approach 2:
Reference datasets serve as an intermediary validation layer between the oracle and the distributed database. This mediator enables automated verification of measurement data authenticity without requiring complex validation logic at every node, thus preserving storage efficiency while enhancing reliability.
2Reliability
If validation measures are implemented at the distributed database, then data integrity is improved, but system complexity increases due to additional validation processes
Solution Approach 1:
The system creates and stores copies of reference datasets at mining nodes during block validation. This copying approach allows validation to occur using pre-prepared reference data rather than complex real-time computation, thereby improving data integrity while minimizing the addition of system complexity.
3Measurement precision
If multiple validation measures are applied to measurement datasets, then measurement precision is improved, but processing time increases
Solution Approach 1:
Reference datasets are prepared and stored in advance at mining nodes, enabling rapid validation comparison when measurement datasets arrive. This preliminary preparation of validation data allows multiple validation checks to be performed accurately without significant processing delays.
Data Source
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AI summary
The invention relates to a mining node (151-153) of an infrastructure (150) of a distributed database (159), the mining node (151-153) comprising a control circuitry (155-157) configured to: obtain a measurement dataset (91) indicative of one or more observables (85) of an event (81), the measurement dataset (91) comprising processed raw data samples; and to perform a comparison between the measurement dataset and a reference dataset, the reference dataset comprising at least one of one or more predefined constraints (702, 711, 712) associated with the event (81) or a further measurement dataset (92) that is indicative of one or more further observables (86) of the event (81); and depending on a result of the comparison, to selectively trigger one or more validation measures (5005, 5006) for the measurement dataset (91), the one or more validation measures being implemented at the distributed database.