Private Data Feature Intersection via Homomorphic Encryption
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods fail to effectively identify data feature intersections or overlaps between private datasets without revealing specific data items or features, which is crucial for collaborative efforts while maintaining privacy.
Innovation Solution
A computer system and method utilizing natural language processing, lexical optimization, and encryption technologies like homomorphic encryption and secret key sharing to identify data feature intersections between private datasets without exposing specific data items or features, using a third-party facilitator to manage encryption and decryption keys and perform computations on encrypted data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data feature intersection identification is performed between private datasets, then collaborative insights can be obtained, but specific data items and features may be revealed compromising privacy
Solution Approach 1:
A trusted third-party facilitator is introduced as an intermediary to perform the data intersection identification process. The facilitator receives encrypted data from multiple entities, performs the intersection analysis, and returns results without any party being able to view each other's raw data. This mediator approach enables collaborative insights while maintaining privacy through cryptographic protection throughout the process.
Solution Approach 2:
The patent transforms the data representation parameters by converting raw data into encrypted form using homomorphic encryption. This parameter change allows mathematical operations to be performed on encrypted data directly, enabling intersection identification without decrypting the underlying sensitive information. The data remains in encrypted state throughout processing, preventing privacy leakage while preserving analytical capabilities.
2Reliability
If encryption technologies are used to protect privacy during data comparison, then security is improved, but computational complexity increases
Solution Approach 1:
Data is pre-encrypted using homomorphic encryption before being submitted to the trusted facilitator. This preliminary encryption action ensures that privacy protection is established before any processing occurs, and the encrypted data can be directly used in subsequent intersection operations without requiring additional encryption steps during the analysis process.
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with cryptographic operations. Instead of decrypting data for analysis and then re-encrypting results, the system substitutes this mechanical approach with homomorphic encryption that allows direct computation on encrypted data. This substitution reduces the overall computational complexity by eliminating multiple encryption/decryption cycles while maintaining strong privacy protection.
Data Source
AI summary
This disclosure is directed to a computer system and method to assist in identifying data feature intersection or overlap between private datasets without revealing any specific data items or data features in the datasets. Various technical components including natural language processing, lexical optimization, and encryption and key management technologies such as homomorphic encryption and secret sharing and coding, are integrated into the disclosed system and method to achieve the data feature intersection identification. Such a system and method may be employed in circumstances where data feature intersection is important for collaborative efforts between entities.