Secure Stream Processing for Medical Data.

Clicks: 386
ID: 83198
2019
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Ranked #18 of 127 articles by views in conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference

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Abstract
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.
Reference Key
segarra2019secureconference Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Segarra, Carlos;Muntane, Enric;Lemay, Mathieu;Schiavoni, Valerio;Delgado-Gonzalo, Ricard;
Journal conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference
Year 2019
DOI
10.1109/EMBC.2019.8856334
URL
Keywords
random forest iot wsn amof intrusion detection systems linear regression

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