Join Constraint Compilation for Privacy-Safe Clean Room Queries
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Solution Overview
Problem
Traditional data clean room systems face challenges with managing query constraints and privacy policies, leading to inflexibility, inefficiency, and vulnerability to attacks, particularly in cross-platform data collaboration and large-scale deployments.
Innovation Solution
A data platform integrates join constraint policies into datasets, allowing users to define and enforce join constraints using a unified interface, supporting full SQL queries while ensuring privacy and governance, and applying policies at query compilation time across different cloud service providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data clean room systems are used to manage query constraints and privacy policies, then data security and privacy protection are maintained, but system flexibility and operational efficiency deteriorate
Solution Approach 1:
The patent introduces a constraint system as an intermediary layer between the query processor and data storage. This constraint system includes a constraint compiler that translates high-level query constraints into low-level executable constraints, and a constraint evaluator that enforces these constraints during query execution. This intermediary architecture maintains security while improving flexibility by allowing users to define constraints in a manageable format that is automatically compiled and enforced.
Solution Approach 2:
The patent segments the constraint management system into distinct components: constraint definition interfaces, constraint compilation modules, constraint evaluation engines, and policy management systems. This segmentation allows each component to be optimized independently - the compilation layer handles flexibility and ease of operation, while the evaluation layer ensures security and reliability, thus resolving the contradiction between these two requirements.
2Reliability
If traditional data clean room systems are used to manage query constraints and privacy policies, then data privacy protection is ensured, but processing efficiency and productivity deteriorate
Solution Approach 1:
The patent implements constraint compilation that occurs before query execution. The constraint compiler pre-processes and validates constraints, generating optimized execution plans that embed privacy protection logic directly into the query execution pipeline. This preliminary action ensures privacy protection is maintained while avoiding the overhead of runtime constraint checking, thus improving processing efficiency.
Solution Approach 2:
The patent replaces traditional mechanical constraint checking mechanisms with an optimized constraint evaluation engine that uses vectorized operations and cache-friendly data structures. This substitution maintains rigorous privacy protection while significantly improving processing throughput by reducing the computational overhead associated with constraint enforcement during query execution.
3Reliability
If traditional data clean room systems are used to manage query constraints and privacy policies, then data security is maintained, but system complexity and vulnerability to attacks increase
Solution Approach 1:
The patent implements a universal constraint system that handles multiple types of constraints (privacy policies, query constraints, access controls) through a single unified architecture. The constraint compiler and evaluator are designed to process various constraint types using the same core mechanisms, reducing system complexity by eliminating the need for separate enforcement mechanisms for different security requirements while maintaining comprehensive data protection.
4Reliability
If traditional data clean room systems are used to manage query constraints and privacy policies, then privacy control is provided, but operational efficiency and resource utilization deteriorate
Solution Approach 1:
The patent employs parameter optimization techniques where the constraint evaluation engine dynamically adjusts evaluation parameters based on query characteristics and data distribution. The constraint compiler generates optimized execution plans that adjust threshold parameters, selection criteria, and evaluation strategies to minimize computational resource consumption while maintaining strict privacy control, thus resolving the contradiction between privacy enforcement and operational efficiency.
Data Source
AI summary
Described is a system for join constraints for query processing by receiving a first query directed towards a shared dataset in a data clean room; assessing the first query to identify that the one or more functions s at least a join function; determining that the first query is configured to join a first set of data from the shared dataset with a second set of data using the join function; determining that a join constraint policy is to be enforced in relation to the first query; and generating an output to the first query based on the execution of the one or more functions, the output to the first query without data values stored in the portion of the first set of data based on determining that the join constraint policy is to be enforced in relation to the first query.