Secure Stream Processing for Medical Data
This addresses privacy concerns for medical data processing in unauthorized clouds, though it is incremental as it builds on existing technologies like trusted hardware and Spark.
The paper tackles the problem of securely processing medical data in the cloud by presenting a proof-of-concept streaming IoT architecture that combines trusted hardware and Spark to process cardiac data, showing that adding privacy doubles execution time compared to standard Spark Streaming.
Medical data belongs to whom it produces it. In an increasing manner, this data is usually processed in unauthorized third-party clouds that should never have the opportunity to access it. Moreover, recent data protection regulations (e.g., GDPR) pave the way towards the development of privacy-preserving processing techniques. In this paper, we present a proof of concept of a streaming IoT architecture that securely processes cardiac data in the cloud combining trusted hardware and Spark. The additional security guarantees come with no changes to the application's code in the server. We tested the system with a database containing ECGs from wearable devices comprised of 8 healthy males performing a standarized range of in-lab physisical activities (e.g., run, walk, bike). We show that, when compared with standard Spark Streaming, the addition of privacy comes at the cost of doubling the execution time.