Decentralized Federated Data Sharing With Policy-Based Query Access
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Solution Overview
Problem
Existing federated data sharing systems often require a central node, lack user-specific access control, and incur significant computational overhead, leading to inefficiencies and security concerns in managing and sharing big data across multiple organizations.
Innovation Solution
A decentralized platform that enables secure, federated data ownership, storage, and usage by allowing organizations to set data policies and control access through a network of servers, facilitating data sharing while maintaining data integrity and compliance with privacy regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a central service is used to integrate information from multiple disparate sources, then data sharing and searching capabilities are improved, but system complexity and security risks increase
Solution Approach 1:
The system segments the federated data sharing functionality into distributed server nodes that operate independently. Each server maintains local data sources and participates in federated queries without requiring a centralized coordination point, thus reducing system complexity while maintaining adaptability.
Solution Approach 2:
The patent introduces an escrow server as an intermediary that facilitates data sharing operations without centralizing control. The escrow server acts as a neutral mediator that enables trusted execution environments and secure data exchange between disparate organizations, improving data sharing capability while avoiding the complexity of a fully centralized architecture.
2Ease of operation
If data is shared across multiple organizations with different policies and standards, then data accessibility is improved, but security control and compliance difficulty increase
Solution Approach 1:
The system implements local quality by allowing each participating organization to define and enforce its own data policies, access controls, and compliance standards at its local server nodes. This enables data accessibility across organizations while maintaining granular security control tailored to each organization's specific requirements.
Solution Approach 2:
The patent implements preliminary action through pre-defined data policies and access control rules that are established before data sharing operations. These policies are embedded in the federated query execution process, automatically enforcing security and compliance requirements without requiring real-time complex negotiations between organizations.
3Adaptability or versatility
If federated queries are executed across distributed data sources, then data integration capability is improved, but computational overhead increases
Solution Approach 1:
The system extracts and executes computationally intensive operations locally at the data source servers rather than centralizing them. Federated queries are processed by executing SQL operations directly against local data sources, extracting only the necessary results for further integration, thus reducing overall computational overhead while maintaining data integration capability.
Solution Approach 2:
The patent uses copying by creating virtual representations of data through federated queries without physically moving or duplicating large volumes of data. The system copies query results and metadata rather than entire data sets, reducing computational overhead while maintaining the ability to integrate data from distributed sources.
4Device complexity
If user permission settings are not implemented at nodes, then system simplicity is maintained, but data security and access control capability deteriorate
Solution Approach 1:
The system implements dynamic access control where permission settings at individual nodes can be adjusted based on user roles, data sensitivity, and query context. This dynamic approach allows the system to maintain simplicity in normal operations while providing robust security controls when needed, resolving the contradiction between system simplicity and data security.
Data Source
AI summary
Methods and systems for secure, federated, and decentralized ownership, storage, sharing and usage of big data are provided. According to one example, a first server maintains access to a plurality of federated data sources including at least one local database and at least one remote database. The local database is subject to a first data policy controlled by the first server and the remote database is subject to a second data policy controlled by a remote server. The first server receives a query from a user electronic device and verifies at least one permission attribute of the user electronic device relative to the data policies. After the federated data operation query has been approved, the first server generates results of the query including data fields from the local and remote databases in compliance with the first and second data policies.