Multi-tenant Data Sharing Platform for Clinical Research
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Solution Overview
Problem
Current data management systems lack the ability to efficiently and securely share and analyze data across multiple tenant organizations while enforcing customized access policies, leading to limitations in data utilization and collaboration.
Innovation Solution
A multi-tenant data access platform that stores organization hierarchy and policy data to provide differentiated access to data, allowing for selective sharing and analysis while enforcing access restrictions, enabling machine learning tasks and data aggregation without revealing sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is shared across multiple tenant organizations, then data utilization and collaboration are improved, but data security and privacy protection deteriorate
Solution Approach 1:
The patent segments data access permissions by organizing tenant hierarchies into multiple levels (organization level, study level, participant level). Each level has distinct access policies that control what data can be shared and with whom. This segmentation allows data to be shared across tenants while maintaining granular control over privacy-sensitive information.
Solution Approach 2:
The patent introduces a data sharing platform as an intermediary between tenant organizations. This platform acts as a mediator that enforces access policies, authentication mechanisms, and authorization rules. The intermediary ensures that data sharing occurs securely without direct access between tenants, protecting privacy while enabling collaboration.
2Reliability
If customized access policies are enforced for each tenant organization, then data security is improved, but system complexity and policy management difficulty worsen
Solution Approach 1:
The patent implements a universal policy management framework that handles multiple tenant organizations with different access requirements through a single system. The platform provides multi-functional capabilities including authentication, authorization, policy enforcement, and audit logging that work across all tenants. This universality reduces complexity by providing standardized mechanisms rather than requiring separate custom solutions for each tenant.
Solution Approach 2:
The patent employs dynamic policy enforcement where access permissions are not static but adapt based on user context, data sensitivity, and organizational hierarchy. The system dynamically evaluates access requests against stored policies and adjusts permissions in real-time. This dynamic approach maintains security while reducing the need for manually configuring complex static policies for every scenario.
3Object-affected harmful factors
If machine learning tasks are performed on restricted data sets, then data privacy is improved, but model training quality and statistical analysis accuracy deteriorate
Solution Approach 1:
The patent creates controlled copies of data that can be used for machine learning tasks without exposing the original sensitive data. The system generates synthetic data copies or anonymized versions that preserve statistical properties needed for accurate model training while removing personally identifiable information. This copying mechanism enables high-quality analysis while maintaining privacy protection.
Solution Approach 2:
The platform acts as an intermediary that facilitates machine learning on restricted data by providing controlled access mechanisms. The system allows ML models to process data through secure enclaves or federated learning approaches where the model learns from data without the data leaving the secure environment. This intermediary function enables accurate training while preserving privacy constraints.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable media, for machine learning in a multi-tenant data sharing platform. In some implementations, a server system provides a multi-tenant data sharing platform configured to selectively use stored data collected for different tenant organizations according to policy data for the respective tenant organizations. A request from one organization is received to perform a machine learning task involving a data set of a different tenant organization. The server system uses stored policy data to determine an applicable data policy, and based on the determination, the server system performs the machine learning task and provides the results of the machine learning task.