Privacy-Preserving Healthcare Monitoring using Multi-Authority Attribute-based Encryption and Zero-Knowledge Proofs

Abstract

The field of healthcare monitoring has been revolutionized by a combination of cloud computing and IoT-enabled sensor networks that enable real-time data collection, storage and processing. However, because healthcare data is sensitive, strong security and privacy-preserving measures are required to stop unwanted access and preserve data integrity in multi-authority settings. To propose a collaborative and privacy-preserving sensor cloud architecture for safe, scalable, and fine-grained access control for healthcare monitoring systems by utilizing Zero-Knowledge Proofs (ZKP) and Multi-Authority Attribute-Based Encryption (MA-ABE). The proposed method combines ZKP for authentication and MA-ABE for attribute-based encryption to protect sensitive data. It uses gateways to securely aggregate and transmit data to the cloud, and elliptic curve cryptography to optimize performance. The framework decreased computational overhead by 40% while achieving notable gains in encryption (120 ms) and decryption (150 ms). Outperforming current approaches in healthcare data security and access management, classification accuracy reached 96.5% with improved privacy preservation (96%) and scalability (94%). The suggested system addresses privacy, scalability, and computational efficiency while incorporating cutting-edge cryptographic approaches to provide secure healthcare monitoring. Large-scale, real-time healthcare applications can benefit greatly from its strong solution.