Encrypted Data Synchronization for Cross-Database Recommendations
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Solution Overview
Problem
Existing systems face challenges in securely synchronizing and integrating data across multiple databases associated with different entities while ensuring data security and reducing redundant processing and storage needs.
Innovation Solution
Utilizing homomorphic encryption to process and integrate user data across databases, employing predictive models to generate encrypted recommendations, and decrypting them securely within a computing system associated with the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is synchronized across multiple databases using traditional methods, then data integration is achieved, but data security is compromised
Solution Approach 1:
The system segments data into encrypted portions distributed across multiple databases, with each database holding only a fragment of the complete information. This segmentation allows data integration functionality while maintaining security, as no single database contains the full unencrypted data set.
Solution Approach 2:
Homomorphic encryption acts as an intermediary mechanism that enables computations on encrypted data without decryption. This intermediary layer allows data processing and integration across databases while preserving security, eliminating the need for traditional decryption-based integration methods.
2Reliability
If data is encrypted using homomorphic encryption, then data security is enhanced, but processing speed decreases
Solution Approach 1:
The system performs preliminary encryption of data before storage, converting data into homomorphically encrypted form in advance. This preliminary action enables subsequent processing operations to be performed on the already-encrypted data without requiring additional encryption/decryption cycles during processing, thereby reducing overall processing time.
3Ease of operation
If multiple databases store complete user data sets, then data accessibility is improved, but storage efficiency deteriorates
Solution Approach 1:
User data sets are segmented into encrypted fragments distributed across multiple databases. Each database stores only a portion of the complete data set in encrypted form, reducing redundant storage while maintaining accessibility through cryptographic operations on the distributed fragments.
Data Source
AI summary
A method includes accessing the first user data set and the second user data set. The second user data set includes a homomorphically-encrypted data set. The method includes executing a predictive model trained to generate a homomorphically-encrypted recommendation of an identified interaction for a user based at least in part upon the first user data set and the homomorphically-encrypted data set. The user is associated with at least one of the first entity or the second entity. The method further includes transmitting the homomorphically-encrypted recommendation of the identified interaction for the user to a computing system associated with the second database and the second entity, receiving, from the computing system associated with the second database and the second entity, a decrypted recommendation of the identified interaction for the user, and transmitting the decrypted recommendation to a computing system associated with the user.