Blockchain Ledger Validation for Contextual Data Integrity
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Solution Overview
Problem
Centralized databases face challenges in maintaining data integrity and accessibility across multiple channels due to single-point failures, lack of redundancy, and difficulty in integrating longitudinal data with integrity, especially in complex business processes like financial services with omnichannel interactions.
Innovation Solution
Implementing a blockchain-based decentralized storage system with smart contracts to ensure immutable, secure, and decentralized data management, using a blockchain ledger to link transaction outcomes and inferences data, preserving contextual integrity across channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a centralized database is used to store data at a single location, then data management and control are simplified, but the system becomes vulnerable to single-point failures and lacks redundancy
Solution Approach 1:
The patent segments the centralized database into multiple distributed nodes across a blockchain network. Each node stores copies of the data, eliminating the single-point failure vulnerability while maintaining simplified management through standardized protocols and consensus mechanisms that govern all nodes uniformly.
2Quantity of substance
If data is stored in a centralized database, then data redundancy is minimized with a single primary record, but accessibility and integration across multiple channels are limited
Solution Approach 1:
The blockchain database provides universal accessibility across multiple channels and platforms. The distributed ledger can be accessed by any node in the network through standardized interfaces, enabling omnichannel integration while maintaining data integrity. The system serves multiple functions simultaneously: storage, verification, sharing, and integration across diverse channels.
3Loss of information
If longitudinal records are integrated from multiple channels in a centralized database, then data completeness is improved, but data integrity and contextual consistency become difficult to maintain
Solution Approach 1:
The blockchain implements feedback mechanisms through consensus validation. Before longitudinal records from multiple channels are integrated into the ledger, they undergo verification by network nodes that check for consistency and integrity. This feedback loop ensures that only valid, contextually consistent data is accepted, maintaining precision while achieving completeness.
Solution Approach 2:
The system performs preliminary validation and verification of data before integration into the longitudinal records. Hash values are computed and verified in advance, and consensus is reached on data validity before it is committed to the blockchain. This preliminary action prevents integrity issues from arising during data integration.
4Ease of operation
If a centralized database is used, then security control is concentrated and simplified, but the system lacks decentralization and trust distribution
Solution Approach 1:
The blockchain security model enables self-service through cryptographic authentication and autonomous consensus mechanisms. Each node independently verifies data integrity and maintains security without requiring centralized control. The system distributes trust across all participants, who collectively enforce security rules through predefined protocols, achieving both decentralization and simplified security management.
Data Source
AI summary
An example operation includes one or more of receiving, by a data processing node, inference data object from a multi-channel data server over a blockchain, sorting, by the data processing node, longitudinal records contained in the inference data object, linking, by the data processing node, transaction outcomes and inferences data from the inference data object to the sorted longitudinal records, and recording linked data onto a blockchain ledger. The data processing node serves as a validator of data from a robo-advisory using natural language (NL) processing to reduce bias and measure effectiveness of inference from the robo-advisory.