Privacy-protected data broadcast using zero-knowledge proofs
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Solution Overview
Problem
User devices, particularly wearable devices, face challenges in effectively anonymizing user data due to insufficient privacy controls, leading to reluctance in data sharing despite potential commercial benefits, as existing systems are inefficient and fail to adequately protect user privacy.
Innovation Solution
Implementing a privacy-protecting broadcasting method using aggregated zero-knowledge values in elliptic curve cryptography (ECC) arithmetic circuits or algebraic intermediate representations (AIRs) to anonymize user data, allowing users to share data anonymously in exchange for incentives, while ensuring transparency and trust through zero-knowledge proofs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user data is shared for commercial benefits, then productivity and data utilization improve, but user privacy and data security deteriorate
Solution Approach 1:
The patent introduces zero-knowledge proofs as an intermediary mechanism that enables data verification without exposing the actual data. The proof system acts as a mediator between data sharers and privacy protectors, allowing businesses to verify data authenticity while users maintain control over their personal information. This resolves the contradiction by enabling data utilization through verification without compromising privacy.
Solution Approach 2:
The patent extracts the essential verification capability from the data itself, separating the proof of data validity from the actual sensitive information. By using zero-knowledge proofs, the system extracts only the necessary verification element (the proof) while leaving the sensitive data hidden, thus enabling data utilization without exposing privacy-sensitive content.
2Reliability
If traditional anonymization methods are used, then some privacy protection is achieved, but data utility and measurement precision deteriorate
Solution Approach 1:
The patent changes the fundamental parameter of data representation by transforming raw data into zero-knowledge proofs. This parameter change allows the data to maintain its verification utility while completely altering its form to protect privacy. The transformed data retains mathematical properties necessary for verification while losing all personally identifiable information.
3Quantity of substance
If comprehensive data collection is implemented, then data quantity and analysis capability improve, but privacy control and user trust deteriorate
Solution Approach 1:
The patent segments the data sharing process into distinct components: data collection, proof generation, and verification. This segmentation allows comprehensive data collection to occur while maintaining user control through the proof generation stage. Users can authorize data collection and then verify through zero-knowledge proofs that their data is being used appropriately, thus maintaining both data quantity and privacy control.
Data Source
AI summary
A method includes detecting presence of a user device within a proximity of a geographical location. The method includes requesting user data by providing an incentive to a user of the user device to provide user data. The method includes aggregating the user data with data of other user devices to generate an anonymous set of user data. The method includes providing a service to a user of the user device in response to receiving the user data.