Rapid advancements in digital medical technologies have significantly improved patient care but have also raised complex security and privacy challenges. Traditional tools for detecting vulnerabilities in networked medical devices, primarily used by network administrators and security specialists, have become insufficient due to their large-scale use across the entire healthcare network. Aiming to improve security in healthcare, MedTech Chain proposes a way to solve this challenge by leveraging blockchain and privacy-enhancing technologies, offering an authenticated, decentralised, secure, and privacy-preserving environment for the research and monitoring of medical device data. Currently, the framework enables counting, averaging, and grouped counting queries with multiple filtering capabilities like time frame and location. Such functionalities can provide valuable insights not only for threat intelligence but also for medical research and hospital management. MedTech Chain is modular and flexible, designed to seamlessly extend to new device technologies and research demands. To our knowledge, the approach is among the first to employ ϵ-differential privacy in the context of medical device data.

Employing blockchain and privacy-enhancing technologies, MedTech Chain promises an authenticated, decentralised, secure, and privacy-preserving environment for the real-time research and monitoring of medical device data. Through its querying functionalities, the platform can provide valuable insights for threat intelligence, medical research and hospital management. To our knowledge, the approach is among the first to employ ϵ-differential privacy in the context of medical device data. The current work details the framework's functionality and demonstrates a negligible time overhead induced by ϵ-differential privacy to data analysis.

Employing blockchain and privacy-enhancing technologies, MedTech Chain promises an authenticated, decentralised, secure, and privacy-preserving environment for the real-time research and monitoring of medical device data. Through its querying functionalities, the platform can provide valuable insights for threat intelligence, medical research and hospital management. To our knowledge, the approach is among the first to employ ϵ-differential privacy in the context of medical device data. The current work details the framework’s functionality and demonstrates a negligible time overhead induced by ϵ-differential privacy to data analysis.