An efficient technique for privacy preservation, trusted and secure patient-centric services in smart healthcare

The growing dependence on IoT, Cloud Computing, and mobile technologies in healthcare has heightened the issues surrounding privacy, trust, and security of patient data. In response to this need, this paper introduces a new privacy-preserving and trust-aware framework for patient-centric smart healthcare systems. This framework is a combination of AES and Attribute-Based Encryption (ABE) encryption techniques, various differential privacy approaches, and a dynamic trust evaluation model, using a real-time behaviour-based analytics, and blockchain logging. The system transmits data using TLS protocols to ensure secure data transfer, and offers patient-controlled access with attribute-based policies. Evaluation experiments were performed with both a real-world (MIMIC-III) dataset and synthetic IoT datasets, showing improved trust accuracy of 96.2%, limited data leakage of <1.5%, and strong scalability of +12% latency up to 1000 nodes, and also only had moderate system overhead of 14.3%. A practical scenario demonstrates how the framework can be utilized in healthcare environments in real-time. While identifying the limitations discussed and recognizing the context of potential remedial steps and pursuing insights from this work that can assist in the deployment of a regulatory compliant and secure and efficient data sharing framework in a way that pursues trust and supports and develops intelligent smart digital healthcare ecosystems.