Scalable privacy-preserving data analytics for IoMT via FHE and zk-SNARK-enabled edge aggregation

Nature·Jan 13

✦ NabkaNews BriefAuto-summarized from multiple outlets · verify with the source

Researchers are exploring various methods for secure and private data analytics in the internet of medical things (IoMT), including federated learning, blockchain, and explainable artificial intelligence. Different architectures and frameworks are being proposed, such as multi-layered privacy-preserving architectures and federated microservices architectures, to enable secure medical data exchange and analytics. These approaches aim to enhance security and privacy in healthcare data management, but the specific details and effectiveness of these methods are still being developed and reviewed.

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Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review

Frontiers·May 18

Design of a multi-layered privacy-preserving architecture for secure medical data exchange in cloud environments

Nature·Feb 27

Federated microservices architecture with blockchain for privacy-preserving and scalable healthcare analytics

Nature·Feb 14

Adaptive resource aware and privacy preserving federated edge learning framework for real time internet of medical things applications

Nature·Oct 20

Enhancing security in IoMT using federated TinyGAN for lightweight and accurate malware detection

Nature·Feb 3

Multi-layer encrypted learning for distributed healthcare analytics

Nature·Nov 11

Integrating AI-blockchain framework with Spider Monkey Federated Extreme Learning for enhanced healthcare data protection

Nature·Apr 10

Privacy-preserving cyberthreat detection in decentralized social media with federated cross-modal graph transformers