Unsupervised Neural Network for Physiological Data Authenticity Verification
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Solution Overview
Problem
Healthcare professionals face challenges in verifying the authenticity and privacy of physiological data collected outside medical facilities, such as from wearable devices, due to concerns about data reliability and privacy breaches when sharing sensitive information.
Innovation Solution
A neural network-based data authentication system that learns to extract subject-specific features from physiological signals using unsupervised training with concurrent loss functions, providing an authenticity verification mechanism and privacy preservation by determining the identifiability metric of the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection of data quality is performed to confirm reliability, then data authenticity is improved, but time and resource burden increases significantly
Solution Approach 1:
The patent replaces manual inspection (mechanical human review) with an automated machine learning model that analyzes physiological data streams. The system uses unsupervised learning with multiple loss functions to automatically detect anomalies and verify data authenticity without requiring human time investment, thus resolving the contradiction between maintaining reliability and reducing time burden.
Solution Approach 2:
The system enables self-service authentication where the physiological data itself contains inherent characteristics that the machine learning model uses to verify authenticity. The model learns to identify patterns specific to individual users from the data streams, allowing the data to authenticate itself without external manual verification, thereby improving efficiency while maintaining reliability.
2Productivity
If physiological data is communicated to a central repository for analysis, then data utility is improved, but privacy risk increases due to potential fingerprinting
Solution Approach 1:
The patent extracts only the essential authentication characteristics from the physiological data streams using the machine learning model, rather than storing or communicating the complete raw data sets. The model identifies and extracts specific features that are sufficient for authenticity verification while leaving out unnecessary personal identifiers, thus enabling data utility while reducing privacy risk.
Solution Approach 2:
The system applies different processing and protection levels to different aspects of the data. Sensitive personal identifiers are either removed or heavily protected, while the authentication-critical features are preserved and analyzed. This localized approach to data quality ensures that only necessary information is exposed for analysis, maintaining productivity while minimizing privacy harm.
3Object-affected harmful factors
If de-identification methods are applied to remove personal information, then privacy protection is improved, but data authenticity verification capability deteriorates
Solution Approach 1:
The patent transforms the data from its original form into a different dimensional representation through machine learning feature extraction. The model converts raw physiological signals into abstract feature vectors that capture authentication-relevant patterns while removing direct personal identifiers. This dimensional transformation allows privacy protection through de-identification while preserving the essential characteristics needed for authenticity verification.
Data Source
AI summary
A user device that receives plural strings of data corresponding to physiological data, generated from a training model trained in an unsupervised manner using concurrently implemented loss functions, and using a distance metric, relative to a threshold, applied between the plural strings to provide an indication of authenticity.