Trusted Medical Data Sharing Framework for Edge Computing: A Review Analysis
Keywords:
Auditability, Blockchain, CP-ABE, Edge computing, Federated learning, Medical Data sharing, Privacy, Trusted execution environmentsAbstract
Edge computing is transforming medical data processing by moving computation and storage closer to data sources (wearables, bedside monitors, hospital gateways), reducing latency and bandwidth use while enabling real-time analytics. However, sensitive health data and the distributed, resource-constrained nature of edge environments introduce unique trust, privacy, and interoperability challenges. This review synthesizes the literature on trusted medical data sharing frameworks for edge computing. The author presents a taxonomy of architectural patterns (blockchain/ledger-anchored, attribute-based encryption, federated learning, TEEs, MPC/HE and hybrid stacks), evaluates their security/privacy/performance tradeoffs, identifies practical limitations (scalability, key management, legal compliance), and proposes a prioritized research agenda and engineering best practices. Key conclusions: hybrid architectures that keep PHI local, use privacy-preserving collaborative learning, and record minimal metadata on permissioned ledgers currently offer the best balance for practical deployments, but standard benchmarks, regulatory mapping, and lightweight authorization primitives remain urgent needs.
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