Multi-participant Pipelines in Data Trustee Environments
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Solution Overview
Problem
Current data sharing techniques face challenges in ensuring data privacy and facilitating control and access, particularly in industries like healthcare and banking, where data privacy and confidentiality are paramount, and there are concerns about competitive advantage and regulatory compliance.
Innovation Solution
The development of techniques for constructing, configuring, and executing multi-participant pipelines that access and utilize shielded assets within a data trustee environment, governed by collaborative intelligence contracts, allowing for secure sharing and usage of data while enforcing entitlements and constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is shared to bridge gaps in datasets and generate collaborative intelligence, then the quality and completeness of intelligence improves, but data privacy and confidentiality are compromised
Solution Approach 1:
The patent introduces a data trustee environment as an intermediary between data owners and data users. This mediator enables collaborative intelligence generation by processing and analyzing data without exposing the actual data to either party. The trustee environment acts as a secure buffer that allows data sharing benefits while maintaining privacy through architectural isolation.
Solution Approach 2:
The patent creates virtual copies of data environments within the data trustee boundary. Instead of sharing actual data, the system generates replicated data structures and computational environments that can be accessed and processed by multiple participants without transferring or exposing the underlying sensitive information.
2Productivity
If data is shared openly to enable collaborative intelligence, then productivity and innovation improve, but control and access management become difficult
Solution Approach 1:
The data trustee environment provides a universal platform that handles multiple functions simultaneously: data storage, processing, access control, and result distribution. This multi-functional architecture enables collaborative intelligence generation while embedding access control mechanisms directly into the platform infrastructure, reducing operational complexity.
Solution Approach 2:
The system implements automated access control where the data trustee environment self-manages permissions, authentication, and data protection policies. Participants can define their own access rules and the system automatically enforces them without requiring manual intervention, simplifying control while enabling broad collaboration.
3Reliability
If shielded assets are restricted to exist only within data trustee environment, then data privacy is ensured, but pipeline complexity increases due to cross-environment requirements
Solution Approach 1:
The patent segments pipelines into distinct components: pre-processing stages that can occur outside the trustee environment, core processing stages that must occur within the shielded environment, and post-processing stages that can occur externally. This segmentation allows complex cross-environment pipelines to be built from modular, manageable segments with clearly defined boundaries and interfaces.
Solution Approach 2:
The system implements nested data trustee environments where smaller trusted execution environments can be embedded within larger ones. This nesting allows multi-layered security boundaries where inner environments can access outer environments under controlled conditions, enabling complex cross-environment pipelines while maintaining privacy through hierarchical isolation.
Data Source
AI summary
Embodiments are directed to techniques for constructing, configuring, triggering, and executing various types of multi-party pipelines that access and/or use a shielded asset required to exist or execute within a data trustee environment. Generally, authorized participants can build upon template data privacy pipelines and other shielded assets to create other pipelines. Building blocks such as entitlements, cross-environment pipelines, and/or shielded assets governed by various collaborative intelligence contracts can be used to construct more complicated pipelines that may include any number of data privacy pipelines, cross-environment pipelines, input datasets, computational steps, output datasets, permissible queries, participants, and/or governing collaborative intelligence contracts. As such, various types of multi-participant pipelines can be constructed, configured, triggered, and executed to generate collaborative intelligence, without exposing shielded assets, underlying raw data or algorithms provided by owners, or collaborative data shielded by the data trustee environment.