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

Area of Science:

• Cybersecurity
• Health Informatics
• Applied Cryptography

Background:

• The Internet of Medical Things (IoMT) facilitates real-time health monitoring but faces significant privacy and security risks.
• Existing frameworks struggle to balance data confidentiality, verifiability, and computational efficiency in IoMT data aggregation.
• Sensitive physiological data aggregation in IoMT is vulnerable to data leakage, aggregator misconduct, and adversarial attacks.

Purpose of the Study:

• To propose MedGuard, an end-to-end secure data aggregation framework for IoMT.
• To enable complex analytical queries on encrypted data, ensuring privacy and regulatory compliance.
• To eliminate trusted intermediaries and mitigate insider threats through verifiable computation.

Main Methods:

• Integration of Fully Homomorphic Encryption (FHE) using the CKKS scheme.
• Utilization of Groth16 zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) for verifiable computation.
• High-fidelity simulation using OMNeT++ 6.0.1 with a hybrid dataset.

Main Results:

• Achieved 13.3% improvement in end-to-end latency (64.8 ms) compared to baselines.
• Demonstrated high communication efficiency (1.465 GB/s) and low energy consumption (1.489 mJ per query).
• Sustained high throughputs for packets, aggregates, and queries, ensuring scalability.

Conclusions:

• MedGuard provides a scalable, verifiable, and privacy-preserving solution for IoMT data analytics.
• The framework effectively addresses critical security and privacy challenges in smart healthcare.
• MedGuard supports advanced analytics directly on encrypted data, enhancing clinical decision-making.