Harnessing Block chain Technology and Federated Learning Integration for Addressing Data Security and Privacy in Healthcare

Abstract

The deployment of block chain technology in the healthcare sector has greatly increased. A brand-new, revolutionary method of authenticating and storing data is block chain technology. It is a transparent, secure, and unchangeable distributed ledger system. When it comes to its potential to change and disrupt the way we conduct business and keep data, block chain have been compared to the internet. Block chain offers multiple benefits over conventional systems, including improved security, openness, and efficiency. The potential benefits of block chain technology include cost savings, increased trust, and new business prospects. Block chain technology can be implemented to the healthcare industry to increase data security, decrease fraud and errors, and the improvement of medical records accuracy. In order to improve accuracy in diagnostics and treatment, there is potential for blockchain technology to be integrated with other advanced technologies like AI and ML. A potent technology Embodied in Federated learning such as this allows multiple users to collaboratively train a model while keeping their data secure. Particular emphasis was placed on the aspects of the integration of the technology into the field of healthcare especially in the areas of hospitals, pharmaceuticals, patient information, data security and privacy as well as health data aggregating and sharing. Survey data indicates that federated learning can be employed to protect data in transit or at rest against attacks from rogue nodes in scenarios involving healthcare providers, pharmacies, and patients. In such scenarios, data is collected through federated learning where it is stored in an encrypted form and scattered around various nodes in the network. Thus, adverse nodes are unable to reach the data, which allows them to possess no means to decode the data. Furthermore, federated learning can also be employed to protect the sensitive nature of health data by restricting access to it.