Decentralized Anonymized User Verification System
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Solution Overview
Problem
Users face security risks and unnecessary disclosure of personally identifiable information when verifying characteristics like creditworthiness or identity, as existing methods require sharing sensitive data with third parties that may not need all information.
Innovation Solution
A decentralized and anonymized verification system using machine learning models and blockchain technologies, where only necessary information is shared, and personally identifiable information is anonymized to prevent unauthorized storage and misuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users provide personally identifiable information to third parties for verification, then verification reliability is improved, but security risks and information loss increase
Solution Approach 1:
The patent introduces an intermediary verification system that acts as a mediator between users and service providers. This intermediary uses machine learning models to evaluate user characteristics without requiring direct disclosure of personally identifiable information. The system processes anonymized data through secure evaluation environments, enabling verification while preventing harmful data exposure to third parties.
Solution Approach 2:
The patent extracts only the necessary verification characteristics from user data while leaving personally identifiable information behind. By separating the verification function from the data storage function, the system extracts minimal required information for evaluation purposes, eliminating the need for service providers to store or access sensitive user data directly.
2Measurement precision
If users disclose personally identifiable information for verification, then verification accuracy is improved, but information loss and unnecessary disclosure increase
Solution Approach 1:
The patent segments user information into two distinct categories: personally identifiable information that remains with the user, and verification characteristics that are anonymized and processed separately. This segmentation allows the system to access accurate verification data through machine learning models while preventing unnecessary disclosure of identifying information to service providers.
Solution Approach 2:
The patent creates anonymized copies of user characteristics for verification purposes only. These copies contain the necessary verification accuracy through machine learning processing but lack personally identifiable information. The anonymized copies are used for evaluation while the original sensitive data remains secured with the user.
3Adaptability or versatility
If service providers collect and store user personally identifiable information, then verification capability is improved, but security risks from data storage increase
Solution Approach 1:
The patent extracts the verification capability from the data storage function. Service providers receive only anonymized verification results from the intermediary system, eliminating the need to collect or store personally identifiable information. The verification capability is maintained through machine learning models that process anonymized data without requiring persistent data storage by service providers.
Data Source
AI summary
Disclosed are approaches for verifying user characteristics in a decentralized and anonymized fashion. A first request is received from a client device to verify a characteristic of a user associated with the client device, the request identifying an attribute service and the request comprising personally identifying information (PII) associated with the user. Then, a second request for one or more attributes of the user is sent to the attribute service, wherein the second request comprises at least a portion of the PII associated with the user of the client device. One or more attributes of the user from the attribute service and anonymized. The anonymized attributes and a prompt are then sent to a verifier service, wherein the prompt causes a large language model associated with the verifier service to verify the characteristic of the user based at least in part on the one or more anonymized attributes.