OPRF-Based Privacy-Preserving Data Sharing System
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Solution Overview
Problem
Current solutions for monetizing big data face significant challenges due to privacy concerns and competitive issues, with existing anonymization methods reducing data quality and not fully addressing privacy concerns, and centralized data protection approaches being costly and unresponsive.
Innovation Solution
A system and method using Oblivious Pseudo Random Functions (OPRF) for privacy-preserving insight sharing, allowing data owners to share encrypted insights directly with data seekers, controlling the extent of data sharing through a privacy threshold, without revealing raw data or data structures, thus preserving privacy and data value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anonymization methods (differential privacy, k-anonymity) are used to protect privacy, then privacy concerns are alleviated, but data quality and resolution are reduced
Solution Approach 1:
The patent introduces encrypted data as an intermediary form that allows data to be shared and analyzed without being fully decrypted. The encryption scheme acts as a mediator between the data owner's privacy concerns and the data seeker's need for useful data, enabling operations on data while preserving both privacy and data quality simultaneously through controlled decryption based on privacy thresholds.
2Loss of information
If raw data is shared with data seekers, then data utility and insight quality are improved, but privacy risks and competitive concerns increase
Solution Approach 1:
The patent applies parameter changes by transforming data from raw/unencrypted form to encrypted form, and controlling the decryption process based on privacy threshold parameters. This allows the data to maintain its utility for analysis while changing its state to protect privacy, and selectively restoring clarity only where permitted by the privacy threshold.
3Adaptability or versatility
If centralized third-party data depository is used for privacy protection, then data sharing capability is improved, but operational costs and response time increase
Solution Approach 1:
The patent extracts the essential privacy protection function from the centralized third-party depository model and implements it directly within the distributed system through encryption and threshold-based access control. This eliminates the need for a separate centralized intermediary while maintaining privacy protection capabilities.
4Reliability
If data sharing is restricted to protect privacy, then privacy concerns are addressed, but data monetization and commercialization opportunities are limited
Solution Approach 1:
The patent implements dynamic data sharing where the level of data access and decryption is adjusted based on privacy thresholds and data seeker credentials. This dynamic approach allows data to be shared to varying degrees - fully encrypted for high-value sensitive data, partially decrypted for less sensitive information - enabling monetization opportunities while maintaining privacy protection.
Data Source
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AI summary
The current invention provides a system and method for Data Owners to share with Data Seekers extracted insights from the Big Data, instead of raw data or anonymized raw data, thus reducing or eliminating privacy concerns on the data owned by the Data Owners. An Oblivious Pseudo Random Function (OPRF) is used, with operations using OPRFs occur over encrypted data, thus Data Owners learn only the primary object from Data Seeker and nothing else about the remainder of Data Owners' data. Similarly, Data Seeker learns a list of associated secondary objects and nothing else about Data Owners' data. The extent of sharing can be limited using a predefined threshold depending how much private information Data Owner is willing to share or Data Seeker is willing to pay.