PHDMF

PHDMF manages secure, privacy-preserving sharing and consortium-based meta-analysis of personal health data by combining blockchain consortiums, distributed storage, smart contracts, federated learning, and trusted computing for analysis of genome sequencing, protein expression, and metabolic profile datasets.

Key Features:

• Blockchain Consortium Integration: Establishes a blockchain-based data consortium among medical institutions to enable peer-to-peer data sharing without third-party endorsement and to support flexible consortium membership extension.
• Distributed Storage: Uses distributed storage to keep personal health data primarily at originating institutions, minimizing large-scale data transfers across agencies.
• Distributed Ledger Data Integrity: Records hash values of data entries on a distributed ledger to detect tampering and preserve data integrity and authenticity.
• Smart Contracts for Access and Provenance: Employs smart contracts to manage user access and operations and to provide traceable, auditable transactions for data provenance.
• Trusted Computing Environment for Meta-analysis: Provides a trusted computing environment that enables meta-analysis using statistical summaries rather than original data to protect sensitive datasets.
• Federated Learning Integration: Integrates federated learning with blockchain to enable collaborative model training across institutions without sharing raw personal health data.
• Support for Sensitive Omics and Clinical Data: Explicitly supports sensitive data types including genome sequencing, protein expression, and metabolic profiles.

Scientific Applications:

• Collaborative multi-institutional research: Enables secure sharing and coordinated analysis of personal health data across medical institutions for joint studies.
• Privacy-preserving meta-analysis and federated modeling: Supports meta-analyses and federated learning workflows that operate on statistical summaries instead of raw personal health data.
• Data provenance and integrity auditing: Provides provenance tracking and tamper-detection via ledgered hash values and auditable smart contract transactions.

Methodology:

PHDMF uses a federated approach where personal health data remain at originating institutions with distributed storage; a blockchain-based consortium records hash values on a distributed ledger and enforces access and operations via smart contracts; a trusted computing environment and statistical summaries enable meta-analysis, and federated learning is integrated with blockchain for collaborative model training.