Biometric Health Data Compaction for Clinical Trial Integrity
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Solution Overview
Problem
Current health data management systems in clinical trials face security vulnerabilities due to traditional authentication methods and inefficient data compression, compromising privacy and regulatory compliance, especially in decentralized settings with complex data flow requirements and emergency access needs.
Innovation Solution
A biometric-authenticated system that uses multiple compression codebooks and cryptographic keys derived from patient-specific biometric features for secure, efficient data compaction, integrating multi-modal biometric fusion, liveness detection, and emergency override capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods (passwords, tokens, certificates) are used in health data management systems, then ease of operation is maintained, but security reliability deteriorates due to vulnerabilities to theft, sharing, and compromise
Solution Approach 1:
The patent replaces traditional mechanical authentication systems (passwords, tokens, certificates) with a biometric authentication system using fingerprint sensors and cryptographic key pairs. This substitution eliminates vulnerabilities to theft and sharing while maintaining user convenience through automatic biometric verification.
Solution Approach 2:
The system implements self-service authentication where the patient's own biometric data (fingerprint) serves as the authentication credential. The cryptographic key pair is generated and stored locally on the patient's device, eliminating the need for external authentication servers or manual password management.
2Productivity
If single-algorithm compression is used in health data systems, then device complexity is minimized, but compression efficiency deteriorates for different types of clinical data
Solution Approach 1:
The patent segments clinical data into different categories (primary endpoints, secondary endpoints, safety data, supporting measurements) and applies specialized compression algorithms to each segment. This segmentation allows optimal compression for each data type while maintaining manageable system complexity through modular algorithm selection.
Solution Approach 2:
The system dynamically changes compression parameters and algorithm selection based on the type of clinical data being processed. Different compression ratios, block sizes, and algorithm choices are applied according to data category, achieving high overall compression efficiency without requiring a single complex universal algorithm.
3Adaptability or versatility
If traditional centralized data management is used in clinical trials, then data security is simplified, but adaptability to decentralized multi-site trials and real-time monitoring deteriorates
Solution Approach 1:
The patent implements a universal data management system that functions equally well in centralized and decentralized clinical trial environments. The biometric authentication and cryptographic security framework provides consistent protection across multiple sites, enabling real-time monitoring and distributed data collection without requiring separate security infrastructures.
Solution Approach 2:
The system adds a new dimension of security and control by implementing biometric authentication and cryptographic key management at the patient device level. This vertical layer of security enables decentralized data collection while maintaining centralized oversight capabilities, allowing the system to operate effectively across multiple organizational and geographic dimensions.
4Loss of substance
If high compression ratios are applied to clinical trial data, then data storage and transmission efficiency is improved, but statistical integrity and regulatory compliance deteriorate
Solution Approach 1:
The patent applies different compression quality levels to different types of clinical data based on their importance. Primary endpoint data and safety information maintain higher fidelity with less aggressive compression, while supporting measurements and secondary endpoints can use higher compression ratios. This local quality differentiation preserves statistical integrity for critical data while achieving overall data volume reduction.
Data Source
AI summary
A system and method for biometric-authenticated personal health monitor data compaction with clinical trial optimization is disclosed. The system receives biometric signals from multiple sensor modalities associated with a patient and extracts distinctive biometric features using signal processing algorithms. Patient identity verification is performed by comparing extracted features against stored biometric templates, generating cryptographic keys derived from verified biometric characteristics. Health data is divided into sourceblocks and encoded using multiple compression codebooks enhanced with biometric-derived cryptographic keys. Optimal encoded sourceblocks are selected based on compression efficiency and statistical preservation requirements. A clinical trial data optimization engine classifies health data by type and endpoint significance, determines statistical preservation requirements for regulatory compliance, and validates that compressed data maintains required statistical properties for clinical analysis. The system implements multi-modal biometric fusion, liveness detection, emergency override capabilities, and security controls including role-based access control and audit logging for secure clinical trial data management.