Privacy Pinnacle: Improvising Healthcare Data Security Through Federated Learning and Blockchain Framework

Abstract

Developing intelligent healthcare solutions in the quickly changing world of communication technologies-driven smart cities depends heavily on data. Our work aims to protect user data, which is essential to intelligent healthcare in these kinds of urban areas, in the face of mounting worries about data privacy. However, serious privacy issues are raised by the sensitive nature of health data. Federated Learning (FL) is a potentially useful method that allows cooperative model training over several servers or devices while maintaining localized training data. However, serious privacy issues are raised by the sensitive nature of health data. In this work, we present a novel framework to improve secure healthcare data analytics in Blockchain systems by integrating FL with blockchain technology and the Interplanetary File System (IPFS). To balance privacy and data utility, our method makes use of adaptive noise distribution techniques and dynamic customization. IPFS aids in lowering the cost of data storage, while blockchain technology offers safe and transparent model update aggregation and storage. The results of our experiments show that our approach maintains excellent accuracy with 99.04% for healthcare data security analytics tasks and provides strong privacy protection against different types of attacks. Blockchain integration using Ethereum and IPFS demonstrates the usefulness and viability of our platform.