Privacy-Preserving Brakerski-Gentry-Vaikuntanathan (BGV) Homomorphic Encryption for IoMT Data Security
V. Vinoth Kumar, P. Pabitha · 2024
The rapid proliferation of Internet of Medical Things (IoMT) devices has revolutionized healthcare, offering unprecedented opportunities for remote patient monitoring, personalized treatment, and data-driven insights. However, the sensitive nature of health data collected by IoMT devices necessitates robust security and privacy measures to protect patient confidentiality. In this paper, we propose a novel approach to address these challenges by leveraging blockchain technology in conjunction with the privacy-preserving capabilities of the Brakerski-Gentry-Vaikuntanathan (BGV) homomorphic encryption scheme. Our proposed Blockchain-based Privacy-Preserving BGV Homomorphic Encryption mechanism aims to ensure the confidentiality and integrity of IoMT data while enabling secure computation for data analysis and utilization. Our mechanism employs the BGV homomorphic encryption scheme to enable efficient and secure computation on encrypted data, preserving patient privacy throughout the analysis process. we demonstrate the practical applicability and effectiveness of our approach in safeguarding sensitive health data while facilitating secure and privacy-preserving data analytics. Finally, we discuss potential extensions and future research directions, highlighting the significance of privacy-preserving mechanisms in advancing IoMT data security and enabling the realization of a trusted and interoperable healthcare ecosystem.