Distributed Data Analysis Across Secure Research Environments
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Solution Overview
Problem
The analysis of large data sets, particularly sensitive data such as genomics data, is complicated by the need for organizations to maintain control over their data and the high computational costs associated with data movement and access.
Innovation Solution
A distributed analysis service that connects secure research environments across different organizations, allowing users to perform data analysis operations without copying or transmitting data, using a secure compute environment for result aggregation and ensuring data remains within secure environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is copied or transmitted between organizations for analysis, then data accessibility and analysis capability are improved, but security risks and computational costs increase
Solution Approach 1:
The patent introduces a distributed analysis service as an intermediary layer between organizations and data sources. This service receives analysis requests, translates them into queries for distributed data sources, and returns results without exposing raw data. The intermediary enables cross-organization analysis while maintaining security boundaries, as organizations retain control over their data while still allowing external analysis through the service layer.
Solution Approach 2:
The system segments the analysis process into separate components: query translation, data retrieval from distributed sources, result aggregation, and response delivery. Each component operates independently within security boundaries, allowing analysis functionality to be distributed across multiple organizations without requiring centralized data access. This segmentation enables analysis capability improvement while maintaining organizational data control.
2Adaptability or versatility
If data is copied or transmitted between organizations for analysis, then analysis capability is improved, but computational costs increase
Solution Approach 1:
Instead of copying physical data between systems, the patent copies only the necessary query logic and metadata. The distributed analysis service translates high-level analysis requests into organization-specific queries, retrieves only relevant results, and aggregates them. This approach copies minimal information (queries and results) rather than entire datasets, dramatically reducing computational costs while maintaining analysis capability.
Solution Approach 2:
The system performs partial data retrieval by querying only the specific portions of distributed datasets needed to answer analysis questions. Rather than transmitting or processing entire datasets, the distributed analysis service retrieves only relevant records and aggregates partial results. This partial action approach enables versatile analysis while minimizing computational energy expenditure.
3Reliability
If secure research environments maintain strict data control, then security is improved, but data sharing and collaboration are restricted
Solution Approach 1:
The distributed analysis service provides universal functionality by serving multiple organizations and data sources through a single interface. It handles diverse query types, translates between different data formats and access protocols, and aggregates results from multiple secure environments. This multi-functionality enables collaboration across organizations while each maintains its own security policies and data control mechanisms.
Data Source
AI summary
A method for secure analysis of distributed data includes receiving a request from a user to perform a data analysis operation. The data analysis operation utilizes first data accessible by a first secure research environment and second data accessible by a second secure research environment. The method further includes authenticating the user at the first secure research environment and the second secure research environment, communicating a first query for the first data to the first secure research environment, and communicating a second query for the second data to the second secure research environment. The method further includes receiving, from a secure compute environment in communication with the first secure research environment and the second secure research environment, results from the data analysis operation.