A Dynamic Distributed Architecture for Preserving Privacy of Medical IoT Monitoring Measurements

Medical and general health-related measurements can increasingly be performed via IoT components and protocols, whilst inexpensive sensors allow the capturing of a wider range of parameters in clinical, care, and general health monitoring domains. Measurements must typically be combined to allow e.g. differential diagnosis, and in many cases it is highly desirable to track progression over time or to detect anomalies in care and general monitoring contexts. However, the sensitive nature of such data requires safeguarding, particularly where data is retained by different third parties such as medical device manufacturers for extended periods. This appears to be very challenging especially when standards-based interoperability (i.e using IoT standards like HyperCAT or Web of Things-WoT) is to be achieved. This is because open meta-data of those standards can facilitate inference and source linkage if compiled or analysed by adversaries. Therefore, we propose an architecture of pseudonimyised distributed storage including a dynamic query analyser to protect the privacy of information being released.