Metadata-Driven Sample Selection for Privacy-Preserving ML Training
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Solution Overview
Problem
Machine learning systems face challenges in data selection and feature selection due to data access restrictions and privacy concerns, leading to inefficient training datasets and reduced accuracy.
Innovation Solution
A risk factor management component that selects appropriate data samples and features based on metadata from experienced risk factors, prioritizing patients with more data sources, allowing for the generation of a trained model using privacy-protected data from one facility for use in another, while maintaining data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If engineers and data scientists manually select data samples and features through trial and error due to data access restrictions, then data privacy is protected, but the process becomes time-consuming and difficult
Solution Approach 1:
The system performs preliminary actions by automatically determining metadata from second privacy protected data and using it to select appropriate data samples and features before training begins. This eliminates the need for manual trial-and-error selection while maintaining privacy protection through automated metadata-driven sample determination.
Solution Approach 2:
The system creates a copy of the essential characteristics through metadata extraction from second privacy protected data. Instead of accessing actual sensitive data, the system works with copied feature information (metadata) that preserves the structural properties needed for model training without exposing private information.
2Reliability
If engineers and data scientists manually prepare data based on entity policies, ethics, and vendor requests, then data privacy and ethics are maintained, but the process complexity increases
Solution Approach 1:
The system performs self-service by automatically determining metadata and selecting appropriate data samples without human intervention. The automated system handles policy compliance and ethical considerations through algorithmic sample selection based on metadata, eliminating the need for manual data preparation while maintaining compliance standards.
Solution Approach 2:
Metadata acts as an intermediary between the privacy-protected second data and the training model. The system uses metadata as a mediator to transfer essential feature information without exposing actual sensitive data, simplifying the data preparation process while maintaining privacy and ethical compliance.
3Reliability
If trial and error methods are used for feature selection due to data access restrictions, then data privacy is protected, but ML system creation becomes difficult and less accurate
Solution Approach 1:
The system performs preliminary feature selection by determining metadata from second privacy protected data before model training. This preliminary action identifies the most relevant features automatically, eliminating trial-and-error methods and improving model accuracy through data-driven feature selection that maintains privacy protection.
Solution Approach 2:
The system copies essential feature characteristics through metadata extraction, preserving the informational content needed for accurate model training without accessing actual sensitive data. This copying approach maintains both privacy protection and measurement precision by working with replicated feature properties.
4Reliability
If only partial data access and metadata access are provided to vendors through anonymization, then data privacy is protected, but the quality and applicability of training data decreases
Solution Approach 1:
Metadata serves as an intermediary that bridges the gap between privacy protection and data quality. The system uses metadata to access and utilize essential feature information from anonymized data without exposing actual sensitive information, thereby maintaining both privacy protection and training data quality for effective model training.
Solution Approach 2:
The system creates a copy of the essential data characteristics through metadata extraction from anonymized sources. This copying preserves the structural and informational properties needed for high-quality training data while maintaining privacy protection, eliminating the need for vendors to work with limited anonymized data directly.
Data Source
AI summary
Example implementations described herein are directed to systems and methods for selecting appropriate data samples and features in an access and privacy restricted system. Example implementations involve selection of appropriate samples (e.g. patients) which have enough data sources bringing highly important factors based on the experienced risk factors at other facilities, which is stored as metadata. The risk factor management puts more prioritization on some patients which have more data in the required data source than the other patients among all data sample candidates. The similarity of the training data sample can be a criteria to select new sample sets. Further, the risk factor management selects valuable features effectively based on metadata derived from other facilities. Example implementations help improve machine learning accuracy as part of daily system management in a facility, and can be deployed across facilities without compromising access or privacy restrictions of the data.