Secure Data Asset Sharing System with Permission Control
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Solution Overview
Problem
Existing machine learning technologies face challenges in efficiently utilizing and sharing datasets and models, particularly when they contain proprietary or private data, as building and sharing models can be time-consuming and resource-intensive, and there is a lack of control over permissions for data and model usage.
Innovation Solution
A system that allows dataset and model owners to control permissions for usage, enabling secure sharing and monetization, with options for publishing datasets and models in public galleries as black-box or white-box formats, facilitating prediction-making while maintaining data privacy and allowing fee-based access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If datasets and models are shared publicly to enable broader usage and monetization, then accessibility and utility are improved, but data privacy and security control are compromised
Solution Approach 1:
The patent segments datasets and models into publicly accessible components and privately protected components. Public galleries expose only metadata, summaries, and predictive capabilities without revealing underlying proprietary data or model structures. This segmentation enables broad accessibility while maintaining privacy boundaries between public interfaces and private data assets.
Solution Approach 2:
The patent introduces permission management systems and controlled access mechanisms as intermediaries between users and proprietary data assets. These intermediaries enforce access controls, authentication, and authorization protocols that allow public sharing of predictive capabilities while preventing direct access to sensitive underlying data, thus mediating between accessibility and privacy requirements.
2Reliability
If predictive models are built on large datasets with high quality requirements, then model accuracy and reliability are improved, but time and resource consumption increase
Solution Approach 1:
The patent implements preliminary actions by pre-building, validating, and storing predictive models in public galleries before users need them. Models are pre-processed, tested, and optimized in advance, so users can directly apply them to their data without repeating the time-consuming model building process. This shifts the time investment to an earlier stage when resources are more efficiently utilized.
Solution Approach 2:
The patent enables users to copy and reuse validated predictive models from public galleries rather than building models from scratch. Users can replicate successful models and apply them to their own datasets, significantly reducing the time and computational resources required while maintaining model accuracy through proven methodologies.
3Reliability
If permission control mechanisms are implemented for dataset and model usage, then security and ownership protection are improved, but system complexity and operational overhead increase
Solution Approach 1:
The patent implements self-service permission management where dataset and model owners automatically configure and enforce their own access controls through standardized interfaces. The system provides automated permission inheritance, default access policies, and self-enforcing authentication mechanisms that reduce manual intervention and administrative overhead while maintaining strong security controls.
Data Source
AI summary
A system and method enables users to selectively expose and optionally monetize their data resources, for example on a web site. Data assets such as datasets and models can be exposed by the proprietor on a public gallery for use by others. Fees may be charged, for example, per new model, or per prediction using a model. Users may selectively expose public datasets or public models while keeping their raw data private.