IoT Based Secure Transaction in Remote Patient Monitoring Using Cryptography Techniques

Remote patient monitoring involves using technology to collect health data from individuals in one location and electronically communicating that information to healthcare sources in aaltered location. This allows healthcare professionals to monitor patients' conditions remotely. Health data collected by IoT devices are transmitted over the internet. While data transaction, security challenges are faced in remote patient monitoring .Safeguarding sensitive patient information is crucial to prevent from unauthorized access. In this article purpose cryptographic algorithm is used to prevent data during transaction, AES (Advanced Encryption Standard) and Blowfish algorithm is used in a hybrid encryption approach for securing data in remote patient monitoring. In this paper, IoT devices like SPO2 (blood oxygen saturation), temperature, and heart pulse sensors involves the continuous collection and transmission of vital signs from patients to healthcare providers. The integration of wearable devices and a secure remote monitoring system enhances the ability to detect and address medical issues promptly. Using a hybrid encryption approach with both AES and Blowfish can enhance security, mitigate various Cyber Attack and can do secure transaction in IoT. This combination helps protect against attacks such as brute-force attacks, cryptanalysis, and vulnerabilities specific to each algorithm. Additionally, it adds an extra layer of defense by diversifying the encryption methods, making it more challenging for attackers to exploit potential weaknesses in a single algorithm. The implementation result of this hybrid algorithm ensures confidentiality, integrity, authentication and trust in remote patient monitoring systems. In essence, the future of secure transactions in remote patient monitoring lies in a dynamic, adaptive approach that integrates cutting-edge technologies to fortify the overall cyber security posture and safeguard patient data with the highest level of resilience.