Data Insights Sharing Service for Secure Model Exchange
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Solution Overview
Problem
Existing data analytics services struggle to generate meaningful insights and accurate predictive models for smaller-scale users due to insufficient data, and larger-scale users lack a secure and organized way to share insights and predictive models with broader audiences without exposing sensitive or proprietary data.
Innovation Solution
A data insights sharing service that allows users to share metadata describing their insights and predictive models, enabling others to access and apply these models without exposing the original datasets, while maintaining data security and privacy through authentication and access control mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data marketplaces expose raw datasets to enable sharing, then data accessibility and collaboration are improved, but data security and privacy are compromised
Solution Approach 1:
The patent extracts and shares only the essential elements (insights, predictive models, metadata) from the complete dataset, leaving the sensitive raw data behind. This allows data value to be shared while the harmful exposure of raw data is prevented.
Solution Approach 2:
The system introduces an intermediary layer (the data insights sharing service) that sits between the raw data and potential consumers. This intermediary processes and transforms data into shareable insights without exposing the original sensitive data, mediating between sharing needs and security requirements.
2Device complexity
If smaller-scale users provide insufficient data to analytics services, then data processing simplicity is maintained, but insight quality and model accuracy deteriorate
Solution Approach 1:
The system merges datasets from multiple users through the sharing platform, allowing smaller users to benefit from aggregated data across the network. This combination improves insight quality and model accuracy without requiring individual users to complex data collection efforts.
Solution Approach 2:
The data insights sharing service creates universal access to high-quality insights and predictive models that can be applied by users regardless of their individual data volumes. This multi-functional platform serves both data providers and consumers, enabling smaller users to access enterprise-level analytics capabilities.
3Adaptability or versatility
If larger-scale users share raw datasets to enable collaboration, then data availability for analysis is improved, but data security and proprietary protection deteriorate
Solution Approach 1:
The system creates copies of data value in the form of insights and predictive models that can be shared and applied by multiple users without copying the actual sensitive raw data. These computational copies enable collaboration while maintaining security of the original proprietary data.
Solution Approach 2:
The system transforms the data from its original raw state into a different parameter state (insights, models, metadata) that retains analytical value but removes sensitive information. This parameter transformation enables sharing while protecting security and proprietary rights.
Data Source
AI summary
A method by one or more computing devices implementing a data insights sharing service to allow a first user of the data insights sharing service to share data insights with other users of the data insights sharing service. The method includes storing metadata describing one or more data insights, where the one or more data insights were generated based on analyzing a dataset of the first user, responsive to receiving a request from a second user to access the one or more data insights, generating the one or more data insights based on the metadata describing the one or more data insights without accessing the dataset, and providing the one or more data insights to the second user via a graphical user interface (GUI) of the data insights sharing service.