Oracle Data Verifier for Distributed Ledger Integrity
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Solution Overview
Problem
Blockchain systems rely on oracles to obtain external data, but this assumption of data integrity can be a weakness, as oracles can be subject to exploitation, leading to compromised data that may result in false assumptions or actions by smart contracts.
Innovation Solution
Implementing a verifier system that randomly selects a subset of trusted verifiers to validate data from an oracle by comparing it with data from independent sources, thereby generating a confidence level and ensuring data accuracy before it is stored in a distributed ledger.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from oracles is directly stored in the distributed ledger without verification, then the system operates efficiently with simple data flow, but the reliability of the data is compromised due to potential oracle exploitation
Solution Approach 1:
The patent introduces a verifier as an intermediary component between the oracle and the distributed ledger. This verifier receives data from the oracle, independently obtains data from external sources, compares the two data sets, and only allows storage in the distributed ledger when they match. This intermediary layer ensures data integrity without requiring complex changes to the core blockchain protocol.
Solution Approach 2:
The verification process is performed before data is stored in the distributed ledger. The verifier proactively compares oracle data with externally sourced data in advance, generating a confidence level that determines whether the data should be stored. This preliminary verification prevents potentially compromised data from entering the ledger in the first place.
2Reliability
If multiple verification sources are used to validate oracle data, then the confidence level in data accuracy increases, but the time and computational resources required for verification increase
Solution Approach 1:
The system uses multiple external sources to verify oracle data, which may be more verification than strictly necessary in some cases. By obtaining data from multiple independent external sources and requiring consensus, the system ensures high confidence in data accuracy. The patent accepts the time cost of this excessive verification as necessary to prevent oracle exploitation.
3Object-affected harmful factors
If the system verifies data from oracles by comparing with external sources, then the risk of false data storage is reduced, but the system's operational complexity and resource consumption increase
Solution Approach 1:
The verifier implements a feedback mechanism where the comparison result between oracle data and external source data determines the confidence level. When data matches across sources, the system provides positive feedback by allowing storage. When discrepancies are found, the system provides negative feedback by preventing storage and potentially flagging the oracle for further review. This feedback loop continuously improves system security.
Solution Approach 2:
The verifier independently performs the verification task without requiring manual intervention or complex external validation systems. It autonomously obtains data from external sources, compares it with oracle data, generates confidence levels, and makes storage decisions. This self-service approach reduces operational complexity despite the enhanced verification process.
Data Source
AI summary
Presented herein are techniques for verifying data. A method can include obtaining, from an oracle, a first data set associated with a distributed ledger. The method further includes obtaining a plurality of data sets from a plurality of sources. The method further includes generating a confidence level regarding the first data set for validating the first data set, based on comparing the first data set to the plurality of data sets. The method further includes storing the first data set in the distributed ledger based on the confidence level indicating that the plurality of data sets concurs with the first data set. The method further includes taking a remedial action without storing the first data set in the distributed ledger based on the confidence level indicating a discrepancy between the plurality of data sets and the first data set.