Blockchain Transaction Distribution Analysis for Mining Pool Relationship Detection
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Solution Overview
Problem
In blockchain systems, low-value transactions with low fees often face significant delays in confirmation due to competition from higher-fee transactions, and there is a need to detect special relationships between account owners and mining pools that may influence transaction inclusion.
Innovation Solution
A method and system that perform statistical analysis on transaction distributions across mining pools to identify users with special relationships by comparing their transaction distribution to the statistically expected manner, using a special relationship detection service module to determine if transactions are not distributed in a statistically expected manner, and identifying the specific mining pools involved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If transactions are included in blocks based on mining pool selection, then transaction confirmation speed may improve, but fairness and transparency of the system deteriorates due to potential special relationships
Solution Approach 1:
The system continuously monitors transaction inclusion patterns and provides feedback by identifying special relationships between users and mining pools. This feedback mechanism enables detection of unfair practices while maintaining the speed benefits of mining pool selection, resolving the contradiction between speed and fairness.
Solution Approach 2:
The patent introduces an intermediary analysis layer that sits between the mining pool selection process and the final transaction confirmation. This intermediary detects special relationships and provides transparency, allowing the system to maintain speed while ensuring fairness through monitoring and identification of abnormal patterns.
2Reliability
If statistical analysis is performed to detect special relationships, then system transparency and fairness improve, but computational complexity and processing time increase
Solution Approach 1:
The system uses cryptographic transaction data and block data that already exist in the blockchain, copying only the necessary information for analysis. This approach maintains transparency through statistical analysis while minimizing additional computational complexity by reusing existing data structures.
Solution Approach 2:
The patent transforms complex relationship detection into parameter-based statistical analysis by examining transaction distribution patterns across mining pools. This parameter change simplifies the computational approach while maintaining the ability to detect special relationships, balancing transparency with computational efficiency.
3Productivity
If mining pools have autonomy to select transactions, then processing efficiency improves, but potential for unfair treatment of low-fee transactions worsens
Solution Approach 1:
The system provides feedback on mining pool transaction selection by detecting special relationships and abnormal patterns. This feedback maintains processing efficiency while ensuring fairness, as the monitoring does not interfere with the mining pool's autonomous selection process but rather observes and reports on it.
Solution Approach 2:
The patent enables the system to self-monitor and self-detect unfair practices through automated statistical analysis. Mining pools maintain their efficiency while the system independently checks for fairness, allowing both productivity and ease of operation to coexist through autonomous monitoring.
Data Source
AI summary
An example operation may include one or more processing transactions of a plurality of blocks of a blockchain of the blockchain network to determine a user of a plurality of users that is a party of the respective transaction and a mining pool of one or more mining pools that included the respective transaction in the blockchain, performing a statistical analysis of the transactions to determine if the transactions of a user of the plurality of users is distributed across the one or more mining pools in a statistically expected manner, determining that the user has a special relationship with one or more of the one or more mining pools if the transactions of the user are not distributed across the mining pools in a statistically expected manner, and for a user that is determined to have a special relationship, determining one or more of the mining pools with which the determined user has a special relationship.