Using Trusted Execution Environments for Secure Stream Processing of Medical Data

Abstract

Processing sensitive data, specially medical data produced by body sensors, on third-party untrusted clouds is particularly challenging without compromising the privacy of the users generating it. Typically, these sensors generate large quantities of continuous data in a streaming fashion. Such vast amount of information must be processed efficiently and securely, even under strong adversarial models. The recent introduction in the mass-market of consumer-grade processors with Trusted Execution Environments (TEEs), such as Intel SGX, paves the way to implement solutions that overcome less flexible approaches, such as those atop homomorphic encryption.

This Bachelor Thesis presents MedSpark, a secure streaming processing system built on top of Intel SGX. To showcase the viability of this approach, we use it with a system specifically fitted for medical data. We design and fully implement a prototype system that we evaluate with several realistic datasets. Our experimental results show that MedSpark achieves modest overhead compared to vanilla Spark while offering additional protection guarantees under powerful attackers and threat models.