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Benchmarking the Second Generation of Intel SGX for Machine Learning Workloads

Adrian Lutsch; Gagandeep Singh; Martin Mundt; Ragnar Mogk; Carsten Binnig
In: Birgitta König-Ries; Stefanie Scherzinger; Wolfgang Lehner; Gottfried Vossen (Hrsg.). Datenbanksysteme für Business, Technologie und Web (BTW 2023), 20. Fachtagung des GI-Fachbereichs ,,Datenbanken und Informationssysteme" (DBIS). GI-Fachtagungen (BTW-2023), March 6-10, Dresden, Germany, Pages 711-717, LNI, Vol. P-331, Gesellschaft für Informatik e.V. 2023.


For domains with high data privacy and protection demands, such as health care and finance, outsourcing machine learning tasks often requires additional security measures. Trusted Execution Environments like Intel SGX are a powerful tool to achieve this additional security. Until recently, Intel SGX incurred high performance costs, mainly because it was severely limited in terms of available memory and CPUs. With the second generation of SGX, Intel alleviates these problems. Therefore, we revisit previous use cases for ML secured by SGX and show initial results of a performance study for ML workloads on SGXv2.

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