Encrypted Table Joining Using Equality Masks Under FHE
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Solution Overview
Problem
Existing methods for database table joining under fully homomorphic encryption (FHE) are inefficient, exposing data, noisy, and resource-intensive, particularly in multi-party setups, and do not adequately support equi-joins and range joins without decrypting data.
Innovation Solution
A method involving appending, sorting, and masking encrypted records to generate intersection and not-in-intersection tables, using fully homomorphic encryption operations to create a join table without decrypting data, employing techniques like SIMD operations, rotation, and noise-cleaning polynomials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods for database table joining under FHE are used, then data privacy is maintained, but computational efficiency deteriorates and resource consumption increases
Solution Approach 1:
The patent segments the table joining process into distinct phases: sorting encrypted records, generating equality masks through polynomial operations, identifying intersection tables, and constructing the final join table. This segmentation allows each phase to be optimized independently under FHE constraints, improving overall computational efficiency while maintaining data privacy throughout the process.
Solution Approach 2:
The patent performs preliminary sorting of encrypted records before the actual joining operation. By pre-sorting the encrypted data and pre-computing equality masks using polynomial operations, the system reduces the computational burden during the main joining phase, thereby improving computational efficiency without compromising data privacy.
2Reliability
If existing FHE join methods are used, then data security is preserved, but noise levels increase and resource usage intensifies
Solution Approach 1:
The patent changes the mathematical parameters and operations used in FHE joins, replacing traditional methods with polynomial-based equality mask generation. This parameter change reduces noise accumulation during computations while maintaining the security properties of FHE, as polynomial operations in the encrypted domain generate less noise compared to conventional FHE join approaches.
3Ease of manufacture
If traditional join methods are used, then implementation simplicity is maintained, but data exposure risk increases
Solution Approach 1:
The patent replaces traditional mechanical join operations (which require decryption and direct data comparison) with homomorphic polynomial operations that work directly on encrypted data. This substitution eliminates data exposure risk by keeping all operations in the encrypted domain, while the systematic approach using equality masks maintains implementation clarity.
4Reliability
If FHE operations are performed on encrypted data, then data privacy is maintained, but computational complexity increases
Solution Approach 1:
The patent employs polynomial operations that serve multiple functions simultaneously: they enable equality comparison, generate equality masks, and support intersection identification all within the encrypted domain. This multi-functionality reduces the number of separate FHE operations needed, thereby lowering overall computational complexity while maintaining data privacy.
Data Source
AI summary
An embodiment appends, into a concatenated table, a second plurality of records in a second table to a first plurality of records in a first table. An embodiment sorts, according to each identification value in the concatenated table, the concatenated table. An embodiment generates, using an equality mask derived from each identification value in the sorted table, an intersection table, the intersection table comprising a record in the first plurality of records with a first identifier value matching a second identifier value in a record in the second plurality of records. An embodiment generates, using a not-in-intersection mask derived from the equality mask, a not-in-intersection table. An embodiment adds contents of the intersection table and contents of the not-in-intersection table together, resulting in a join table.