# Secure Stream Processing for Medical Data

## TL;DR

This paper presents a privacy-preserving streaming architecture for medical data that combines trusted hardware and Spark, demonstrating secure processing of ECG data with manageable performance trade-offs.

## Contribution

It introduces a novel IoT streaming architecture that enhances privacy in medical data processing without requiring changes to existing server code.

## Key findings

- Secure processing of ECG data in the cloud using trusted hardware and Spark.
- Privacy features double the execution time compared to standard Spark Streaming.
- Validated system with real wearable device data from healthy volunteers.

## 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 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.