A Secure Data Sharing Framework for AI-Driven Healthcare IoT Networks with Privacy Preservation

Abstract

The Internet of Things (IoT), a paradigm that makes it possible to connect the real and digital worlds, has revolutionized several industries, including healthcare, thanks to the quick development of technology. By making it simple to assess medical parameters using smart devices, IoT has had a huge impact on healthcare and led to the collection of enormous amounts of patient-specific medical data. However, there are security issues associated with this wealth of data. Complex data encryption algorithms are difficult to deploy on IoT devices due to their physical limitations, and there is a need to reduce the computational cost of current cryptographic security techniques. IoT systems must also be resistant to a variety of assaults, including differential, linear, and algebraic assaults.  This study develops a complete architecture that aims to ensure secure data exchange within AI-driven Healthcare IoT networks while protecting patient privacy in order to overcome these difficulties. The framework uses cutting-edge cryptographic methods, access control systems, and decentralized technologies to guarantee the availability, confidentiality, and integrity of data. The suggested solution uses a multi-layered strategy to safeguard private medical data from unwanted access while enabling researchers and healthcare professionals to use AI for data analysis without jeopardizing patient privacy.  An analysis of the framework's performance in comparison to other approaches demonstrates its superiority in terms of maintaining security and privacy. The security issues surrounding healthcare IoT data can be addressed with this suggested system, which enables secure data sharing and analysis while preserving patient privacy.