Securing wearable healthcare devices: An active data framework

This paper tackles the pressing security challenges of wearable healthcare devices within the internet of medical things, which are especially vulnerable due to limited processing power and reliance on wireless communication. We introduce the active data framework, a novel paradigm in which data itself is endowed with embedded logic and metadata to autonomously detect anomalies and trigger adaptive security responses based on context. The key strengths of this approach lie in its fine-grained, dynamic, and lightweight protection strategy, which differs from conventional static frameworks by enabling data to react in real time to threats. The framework combines lightweight cryptography, obfuscation techniques, and a context-aware anomaly detection module, all optimized for resource-constrained environments. We validate the approach through a real-world case study on fall detection with automatic alerting, demonstrating that the system maintains high performance and responsiveness while significantly enhancing data integrity and security. These results underline the potential of active data to advance trustworthy, autonomous, and efficient security for future internet of medical things applications. • Developed an Active Data Framework for securing Internet of Medical Things devices. • Integrated dynamic context detection and adaptive security measures. • Enhanced data security while maintaining device performance and efficiency.