Secure Stream Processing for Medical Data
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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 standardized range of in-lab physical 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.
EMBC 2019, Berlin (Germany), pp. 3450-3453