Cross-Chain Transaction Analysis via HTLC Secret Hashes
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Solution Overview
Problem
Managing transactions across multiple blockchain networks is challenging due to the difficulty in identifying associated transactions and deriving hidden information, particularly in cross-chain transactions, which can lead to issues for financial institutions in tracking customer activities and enforcing regulatory compliance.
Innovation Solution
A computer-implemented method that identifies Hash Time Locked Contract (HTLC) transactions across different blockchain networks by comparing transaction commit times, secret hashes, and expiration times, and derives hidden information such as exchange ratios and parties involved through data mining and visualization of associated transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed ledger systems are used to securely store data in multiple blockchain networks, then data security and immutability are improved, but the ability to identify associated transactions and derive hidden information deteriorates
Solution Approach 1:
The patent introduces an intermediary analysis system that mediates between the secure but opaque blockchain networks and the entities needing information. This system uses HTLC transaction characteristics (secret hashes, time locks, expiration times) as intermediary markers to trace and associate transactions across networks without breaking the underlying security model, thereby recovering hidden information while preserving data security.
Solution Approach 2:
The patent applies metaphorical 'color changes' by using distinctive transaction type identification (HTLC recognition) to highlight and differentiate associated transactions among the general transaction stream. By identifying specific HTLC patterns and their characteristics, the system makes hidden relationships visible and traceable, effectively 'coloring' or marking transactions for easier identification and association.
2Productivity
If atomic swaps are used for trading assets across different blockchain networks without intermediaries, then transaction efficiency is improved, but the ability to trace and identify related transactions deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by analyzing transaction patterns and characteristics (secret hashes, time locks, expiration times) to continuously improve the identification and association of cross-chain transactions. The system provides feedback loops that refine its ability to trace atomic swaps by learning from transaction data, thereby maintaining high transaction efficiency while progressively improving tracing capabilities.
Solution Approach 2:
The patent adds another dimension to transaction analysis by examining HTLC-specific parameters (secret hashes, time locks, expiration times) beyond the standard transaction fields. This dimensional expansion allows the system to trace atomic swaps across different blockchain networks by comparing these additional transaction attributes, making previously undetectable relationships visible without interfering with transaction efficiency.
3Ease of manufacture
If traditional relational databases are used to record transaction history, then ease of implementation is improved, but data immutability and change tracking capability deteriorates
Solution Approach 1:
The patent applies universality by using blockchain technology to serve multiple functions simultaneously: it provides secure immutable storage, enables transparent change tracking through its inherent structure, and maintains ease of access for analysis. By leveraging the multi-functional nature of blockchain (combining database, ledger, and verification capabilities), the system achieves data immutability and change tracking without sacrificing implementation feasibility.
Solution Approach 2:
The patent uses copying by creating and analyzing copies of transaction data across multiple blockchain networks and the analysis system. Instead of modifying original transactions, the system creates analytical copies that can be examined for patterns and associations, preserving the immutability of original blockchain data while enabling comprehensive tracking and analysis of transaction histories.
Data Source
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AI summary
Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for managing transactions in blockchain networks. One of the methods include: accessing transaction data stored in the multiple blockchain networks, the transaction data including transactions published in the multiple blockchain networks, each of the multiple blockchain networks being different from each other, identifying, based on the transactions published in the multiple blockchain networks, multiple cross-chain transactions across the multiple blockchain networks, each of the multiple cross-chain transactions being related to a corresponding set of transactions published on at least two of the blockchain networks, the corresponding set of transactions being associated with one another, deriving hidden information of each of the multiple cross-chain transactions based on an association of the corresponding set of transactions, and deriving hidden information between the multiple cross-chain transactions based on the hidden information of each of the multiple cross-chain transactions.