Secure Multi-Party Query Optimization via Cryptographic Cost Computation
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Solution Overview
Problem
Conventional query optimization methods cannot be applied to database systems with privacy protection needs, as they require disclosure of distribution/statistical information that may compromise data privacy in secure multi-party database systems.
Innovation Solution
A query optimization method for a secure multi-party database involves a central device generating execution plans, determining cost computation formulas, and using secure multi-party computation (MPC) to determine an optimal execution plan without disclosing private data, by employing secure computation methods like private set intersection (PSI) and secret sharing to compute and compare cryptographic results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional query optimization methods are used, then query efficiency is improved, but data privacy is compromised
Solution Approach 1:
The patent introduces a secure multi-party computation framework as an intermediary layer between query optimization and data access. This framework allows multiple parties to jointly compute query costs without revealing their private data distribution statistics, thus resolving the contradiction by enabling efficient optimization while preventing privacy leakage through cryptographic protocols.
Solution Approach 2:
The patent transforms the query optimization process from direct statistical analysis to cryptographic computation. By changing the parameters from raw statistical information to encrypted cost values computed through secure multi-party computation, the system achieves both efficiency and privacy protection simultaneously.
2Ease of operation
If distribution/statistical information is disclosed for query optimization, then query execution path selection is improved, but data privacy security deteriorates
Solution Approach 1:
The patent creates encrypted copies of cost computation results through secure multi-party computation. Each party contributes to computing encrypted cost values without accessing other parties' data, allowing optimization decisions to be made based on these encrypted copies while maintaining privacy security.
Solution Approach 2:
The patent segments the query optimization process into separate computational steps performed by different parties through secure multi-party computation. Each party performs local computations on their data while the overall cost evaluation is distributed, preventing any single party from accessing others' private statistical information.
Data Source
AI summary
Implementations of this specification provide query optimization methods, apparatuses, and systems for secure multi-party databases. In an implementation, a method includes: receiving a current query associated with a plurality of target database of a multi-party database system, generating a plurality of execution plans for the current query, determining, for each execution plan, a respective cost computation formula of a plurality of cost computation values for computing an execution cost of jointly executing the execution plan by the plurality of target databases, receiving a secure computation result from each of a plurality of query engines corresponding to the plurality of target databases, and determining an optimal execution plan having a lowest cost value in the plurality of cost computation formulas based on the secure computation result.