Blockchain Identity Verification With AI Cross-Source Authentication
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Solution Overview
Problem
Current authentication systems are inefficient, insecure, and cumbersome, particularly in verifying user identities for transactions involving retirement accounts.
Innovation Solution
A distributed ledger-based identity verification system that utilizes user-provided identity information, data scraping from websites, and smart contracts to authenticate users by comparing this information with user profiles and internet data, leveraging AI and machine learning models for enhanced security and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication systems are used, then the process is simple and quick, but security and accuracy are insufficient
Solution Approach 1:
The authentication system is divided into multiple independent verification modules: distributed ledger verification, AI-based data scraping, and smart contract validation. Each module handles a specific aspect of identity verification, allowing the system to achieve high security through modular complexity rather than monolithic complexity.
Solution Approach 2:
A distributed ledger acts as an intermediary between the authentication system and multiple data sources. The ledger stores verified identity information and enables secure verification without requiring direct access to sensitive data, thus improving security while managing system complexity through abstraction.
2Measurement precision
If multiple data sources are verified, then authentication accuracy improves, but the verification process becomes more time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-verifying and storing identity information on the distributed ledger before actual authentication occurs. AI models continuously scrape and validate data from multiple sources in advance, so when authentication is needed, the verified data is already available for quick verification.
Solution Approach 2:
The AI-based data scraping and verification process operates continuously in the background, maintaining an up-to-date database of verified identity information. This continuous operation ensures that authentication can proceed quickly without interruption, as the system is constantly preparing verification data from multiple sources.
3Object-affected harmful factors
If comprehensive identity verification is implemented, then fraud risk reduces, but system complexity and processing requirements increase
Solution Approach 1:
The system replaces traditional mechanical verification processes with AI-based automated scraping and analysis. Machine learning models automatically extract, validate, and cross-reference identity information from multiple web sources, reducing the need for manual verification processes and lowering operational complexity despite comprehensive verification coverage.
Solution Approach 2:
The system changes the parameters of verification by transitioning from single-source validation to multi-source cross-validation. By verifying identity information across multiple data sources and using AI models to analyze patterns, the system achieves comprehensive fraud detection while managing complexity through automated parameter transformation and validation.
Data Source
AI summary
The following generally relates to distributed ledger and artificial intelligence (AI) technology, and more particularly relates to identity verification through distributed ledger and/or AI technology. In some embodiments, one or more processors: receive, from a user device of a user, user-provided identity information; scrape, from a website, data of the user; add the scraped data of the user to a distributed ledger; and attempt to authenticate the user by inputting, into a smart contract stored on the distributed ledger: (i) the user-provided identity information, and (ii) the scraped data of the user added to the distributed ledger.