Cryptocurrency Fund Flow Graph Analysis for Entity Identification
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Solution Overview
Problem
The anonymity of cryptocurrency transactions makes it difficult to track transaction flows and identify transaction subjects, posing challenges in detecting illegal activities and preventing the misuse of cryptocurrencies for criminal purposes.
Innovation Solution
A method and system that analyzes cryptocurrency transactions by collecting and standardizing blockchain and multi-type data, constructing network and knowledge graphs, and mapping them to generate a multi-layer transaction analysis knowledge graph, enabling the identification of transaction subjects and patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cryptocurrency transactions use anonymity to protect user privacy, then user privacy is protected, but transaction tracking and subject identification become difficult
Solution Approach 1:
The patent introduces a blockchain analysis system as an intermediary that observes and analyzes cryptocurrency transactions without breaking the anonymity of the blockchain. The system uses graph analysis, clustering algorithms, and heuristic rules to infer relationships between addresses and identify transaction subjects while maintaining the underlying anonymity structure. This mediator approach allows privacy protection to coexist with transaction traceability.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring blockchain transactions, updating address clustering and entity identification based on new transaction patterns. The analysis results feed back into improving the tracking accuracy over time, allowing the system to adapt to new transaction methods while maintaining anonymity protection. This creates a dynamic balance between privacy and traceability.
2Loss of information
If cryptocurrency provides transparency through blockchain technology, then transaction transparency is improved, but illegal activities become harder to detect
Solution Approach 1:
The patent replaces manual monitoring of transparent blockchain data with automated analysis systems using machine learning, graph theory, and pattern recognition algorithms. These computational methods process the transparent transaction data to automatically identify suspicious patterns and potential illegal activities, transforming the challenge of data abundance into an opportunity for automated detection.
Solution Approach 2:
The system analyzes transparent blockchain data from multiple dimensions including temporal patterns, network relationships, transaction amounts, and address behavior characteristics. By adding these analytical dimensions to the basic transparent data, the system can identify illegal activities that would be invisible in simple transaction records, turning transparency into a detectable signal.
3Ease of operation
If cryptocurrency enables simple remittance procedures, then transaction simplicity is improved, but fund flow tracking becomes difficult
Solution Approach 1:
The patent segments the complex fund flow tracking problem into manageable components: individual transaction analysis, address clustering, entity identification, and network relationship mapping. By dividing the tracking task into these segments, the system can process simple remittance transactions through a structured analysis pipeline without requiring complex manual intervention at each step.
Data Source
AI summary
The present application relates to analyzing a transaction flow of cryptocurrency. A method may include collecting distributed ledger information of a blockchain corresponding to a specific cryptocurrency and standardizing blockchain data extracted from the distributed ledger information, collecting and standardizing multitype data related to the cryptocurrency transaction, constructing a cryptocurrency network graph using the standardized blockchain data, constructing a knowledge graph using the standardized multitype data, mapping the cryptocurrency network graph and the knowledge graph to generate a multi-layer based transaction analysis knowledge graph, searching for transaction information using a first cryptocurrency address for which a fund flow is to be tracked as an input address in the transaction analysis knowledge graph, generating a fund flow graph having the input address and an output address as nodes to track a fund flow, and identifying an attribute of each node included in the fund flow graph using the knowledge graph.