Signed Validation Tokens for Privacy-Preserving Data Verification
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Solution Overview
Problem
Current data sharing technologies fail to effectively preserve privacy and incentivize third-party data sharing in trustless ecosystems, as they often require revealing sensitive information and lack mechanisms for secure validation of user properties without compromising user anonymity.
Innovation Solution
A method utilizing a data provider that maintains a database of property values, issuing signed validations and tokens to verify user properties without revealing actual data, allowing businesses to validate user eligibility for offers while maintaining user anonymity, and using blockchain for secure storage and payment through smart contracts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If property values are shared to validate user properties, then validation accuracy is improved, but user privacy is compromised
Solution Approach 1:
The patent extracts only the necessary validation information from the database without revealing the actual property values. The data provider generates signed validations that prove user eligibility for offers without exposing sensitive property data, thus achieving validation accuracy while preserving privacy.
Solution Approach 2:
The patent introduces signed validations and tokens as intermediaries between the data provider and the second entity. These cryptographic artifacts mediate the verification process, allowing the second entity to confirm user properties without direct access to the underlying property values, thereby resolving the contradiction between validation needs and privacy protection.
2Loss of information
If signed validations are issued without revealing property values, then user privacy is preserved, but the ability to verify user eligibility is reduced
Solution Approach 1:
The patent performs preliminary actions by having the data provider issue signed validations and generate tokens in advance, before the actual verification needs to occur. This preliminary cryptographic preparation ensures that when verification is needed, the second entity can reliably verify user eligibility through the pre-generated signed artifacts without needing access to raw property values.
Solution Approach 2:
The patent creates cryptographic copies (signed validations and tokens) of the verification information. These copies contain all the necessary verification data in encrypted or hashed form, allowing the second entity to verify eligibility without accessing the original property values, thus maintaining both privacy and verification reliability.
3Adaptability or versatility
If data providers share user data with multiple entities, then data utility is improved, but user anonymity is compromised
Solution Approach 1:
The patent extracts and shares only the minimal necessary information for each verification purpose. Instead of sharing complete user profiles, the data provider issues specific signed validations for particular offers or purposes, allowing multiple entities to utilize the data for different purposes while each entity receives only the specific validation needed, thereby preserving user anonymity.
Solution Approach 2:
The patent applies different levels of data disclosure to different entities based on their specific needs. Each second entity receives a signed validation tailored to its particular offer or verification requirement, rather than receiving all user data. This localized approach allows high data utility for each entity while collectively preserving user anonymity across the system.
Data Source
AI summary
Methods and systems relating incentivizing a data provider to participate in a match making protocol between a business (second entity) to a user (first entity) are shown. Encryption techniques maintain the secrecy of the data providers data such as proprietary analytics of user information such that the data is need not be shared with users or businesses. Businesses can verify that the user has desired properties without learning the actual raw data owned by the data provider. Users initiate data sharing by explicit request but do not learn the actual raw data known to the data provider, only whether or not they satisfy the properties of interest. The data provider is incentivized because the business compensates the data provider for access to proofs of properties about user data.