Blockchain Re-Hashing with Large Codeword Models After Block Compromise
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Solution Overview
Problem
Existing blockchain systems face challenges in maintaining security and integrity when a single block is compromised, often requiring disruptive methods like forking or reversing transactions, and the computational resources needed to secure large and complex networks are significant.
Innovation Solution
A system using Large Codeword Models (LCM) tokenizes blockchain data into semantic units, applies deep learning to compress and re-secure the data by re-encoding and rehashing the chain, preserving metadata for compatibility with existing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods (forking or reversing transactions) are used to handle compromised blocks, then blockchain security can be restored, but the process becomes complex and disruptive
Solution Approach 1:
The patent replaces traditional mechanical blockchain operations (forking, reversing transactions) with a deep learning-based system that uses Large Codeword Models to detect, compress, and regenerate blockchain data. The LCM processes the blockchain as a sequence of tokens, automatically identifying compromised blocks and generating corrections through neural network predictions, thereby substituting complex mechanical procedures with an intelligent automated system.
Solution Approach 2:
The system enables the blockchain to self-diagnose and self-repair compromised blocks. The deep learning model continuously monitors the blockchain, detects anomalies in blocks, and automatically generates corrections by predicting the intended valid block content based on contextual patterns learned from the surrounding blocks, eliminating the need for external manual intervention or complex consensus protocols.
2Reliability
If traditional methods are used to secure compromised blocks, then security can be restored, but transaction disruption occurs
Solution Approach 1:
The system performs preliminary detection and compression of the blockchain data using the Large Codeword Model before any security restoration is needed. By continuously maintaining a compressed representation of the blockchain through deep learning, the system is prepared to rapidly generate corrections for compromised blocks without disrupting ongoing transactions, as the correction mechanism is pre-established through the learned patterns.
3Quantity of substance
If blockchain networks grow in size and complexity, then more data can be stored and processed, but computational resources required to maintain and secure them increase significantly
Solution Approach 1:
The patent extracts the essential semantic information from the blockchain by representing it as a sequence of tokens processed by the Large Codeword Model. This tokenization approach separates the critical data elements from the full blockchain structure, allowing the deep learning system to work with a condensed representation that requires significantly fewer computational resources while maintaining the ability to detect and correct security issues.
Solution Approach 2:
The system changes the parameter representation of blockchain data from raw transaction records to compressed token sequences processed by neural networks. By transforming the data into a lower-dimensional semantic space through the LCM, the computational complexity of analyzing and securing the blockchain is reduced, enabling efficient processing even as the blockchain grows in size.
Data Source
AI summary
Compressing and re-securing blockchain data using a large codeword model (LCM) with deep learning. The LCM tokenizes the blockchain into sourceblocks, assigns unique codewords to each sourceblock, and processes the codewords through a deep learning core, enabling efficient compression, semantic understanding, and generation of blockchain data. In the event of a compromised block, the system re-encodes and rehashes the entire compressed chain, generating a new secured chain while preserving the original chain as metadata for backward compatibility. The LCM-based approach enhances security, efficiency, and resilience of blockchain networks, offering significant advantages over existing techniques.