Lutsch, Adrian ; Singh, Gagandeep ; Mundt, Martin ; Mogk, Ragnar ; Binnig, Carsten
Hrsg.: König-Ries, Birgitta ; Scherzinger, Stefanie ; Lehner, Wolfgang ; Vossen, Gottfried (2023)
Benchmarking the Second Generation of Intel SGX for Machine Learning Workloads.
20. Fachtagung des GI-Fachbereichs ,,Datenbanken und Informationssysteme" (DBIS). Dresden, Germany (06.-10.03.2023)
doi: 10.18420/BTW2023-44
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Herausgeber: | König-Ries, Birgitta ; Scherzinger, Stefanie ; Lehner, Wolfgang ; Vossen, Gottfried |
| Autor(en): | Lutsch, Adrian ; Singh, Gagandeep ; Mundt, Martin ; Mogk, Ragnar ; Binnig, Carsten |
| Art des Eintrags: | Bibliographie |
| Titel: | Benchmarking the Second Generation of Intel SGX for Machine Learning Workloads |
| Sprache: | Englisch |
| Publikationsjahr: | 10 März 2023 |
| Verlag: | Gesellschaft für Informatik e.V. |
| Buchtitel: | Datenbanksysteme für Business, Technologie und Web (BTW 2023) |
| Reihe: | Lecture Notes in Informatics |
| Band einer Reihe: | P-331 |
| Veranstaltungstitel: | 20. Fachtagung des GI-Fachbereichs ,,Datenbanken und Informationssysteme" (DBIS) |
| Veranstaltungsort: | Dresden, Germany |
| Veranstaltungsdatum: | 06.-10.03.2023 |
| DOI: | 10.18420/BTW2023-44 |
| Kurzbeschreibung (Abstract): | 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. |
| Freie Schlagworte: | systems_funding_50100474, systems_bmwk_safefbdc |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Data and AI Systems |
| Hinterlegungsdatum: | 24 Jul 2023 13:03 |
| Letzte Änderung: | 25 Jul 2023 15:55 |
| PPN: | 509917801 |
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| Suche nach Titel in: | TUfind oder in Google |
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