Secure Data Sharing and Privacy Preservation in AI-Driven Healthcare IoT Networks

Abstract

The healthcare industry has seen a total transformation thanks to the Internet of Things (IoT) networks powered by artificial intelligence (AI). These networks now provide previously unimaginable opportunities for remote monitoring, customized care, and immediate health supervision. However, this quick development has also led to significant worries about the security and privacy of private medical data transferred inside these networks.  By using its expertise to enable seamless data flow within Healthcare Monitoring Systems (HMS), cloud computing has the potential to improve healthcare services[1]. In turn, this enables users to access health-related data regardless of location, including patients, physicians, pharmacists, and insurance agents. Security issues, though, make it difficult to integrate cloud computing into HMS.  These security issues are caused by a lack of knowledge about data storage and the precise security measures used to protect data privacy. People who struggle with diseases like AIDS or other socially significant illnesses, for example, show considerable aversion to such systems and look for a highly reputable HMS that can securely store such sensitive data[2].  This study proposes a fresh strategy: the use of healthcare monitoring systems on the Aneka Cloud platform. With the help of the internet, this solution makes it possible to store, retrieve, and process patients' Electronic Health Records (EHRs) on the Cloud. Additionally, it rapidly alerts emergency agencies, doctors, and family members in urgent situations. On the mobile Cloud platform, a disease diagnosis functionality for Android is also being developed. This invention makes it easier to diagnose diseases using the symptoms that patients report, and it is especially useful for people who live in distant or underserved areas with little access to medical services.