Native Data Clean Room Access Using Query Templates and Privacy Controls
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Solution Overview
Problem
Existing technologies face challenges in securely and efficiently sharing data between different database datasets, particularly in creating target groups for advertising or marketing efforts, as they struggle with privacy concerns and the complexity of data overlap analysis.
Innovation Solution
A data clean room system implements query templates and differential privacy mechanisms to enable secure data sharing, using a native application version of the clean room with stored procedures and privacy mechanisms like differential privacy to protect sensitive data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is shared between different database datasets, then data analysis capability is improved, but security and privacy protection deteriorate
Solution Approach 1:
The patent introduces a data clean room as an intermediary environment between different database datasets. This clean room acts as a mediator that enables data analysis while maintaining security boundaries. The clean room receives data from multiple sources, processes it through controlled query templates, and returns results without exposing raw data, thus resolving the contradiction between data sharing and privacy protection.
Solution Approach 2:
The patent creates virtual copies of data within the clean room environment rather than sharing actual data. Query templates generate synthetic representations that preserve analytical capabilities while preventing access to sensitive original data. This copying approach allows data analysis to proceed without compromising the security and privacy of the source datasets.
2Productivity
If data sharing is enabled between database users, then collaboration efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal data clean room system that serves multiple functions simultaneously. The same clean room infrastructure supports various types of data sharing scenarios, different query templates, and multiple user collaborations. This multi-functional approach enables collaboration efficiency across different use cases without requiring separate complex systems for each scenario, thus managing system complexity while improving collaboration.
Solution Approach 2:
The patent uses query templates with configurable parameters to control data access and processing. By changing parameters within the same template structure, the system can adapt to different collaboration scenarios, data types, and security requirements without modifying the underlying system architecture. This parameter-based flexibility improves collaboration efficiency while maintaining manageable system complexity.
3Adaptability or versatility
If query flexibility is increased for data analysis, then analytical capability is improved, but security control deteriorates
Solution Approach 1:
The patent implements security controls through pre-defined query templates that are established before data analysis occurs. These templates incorporate security rules, access controls, and processing constraints in advance. When users execute queries, they work within the predetermined secure framework, which maintains security control while allowing flexibility within the established boundaries. The preliminary configuration of security parameters resolves the contradiction between query flexibility and security control.
Data Source
AI summary
Embodiments of the present disclosure may provide a data sharing system implemented as a local application of a distributed database. A query from a query template can be validated and executed against shared dataset that comprises portions of data from the database dataset and additional portions of data from another database of the distributed database.