The Price of Privacy: A Performance Study of Confidential Virtual Machines for Database Systems-Reference-Cited by-同舟云学术

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The Price of Privacy: A Performance Study of Confidential Virtual Machines for Database Systems

Published:2024-06-09 Issue: Volume:53 Page:1-8
ISSN:
Container-title:Proceedings of the 20th International Workshop on Data Management on New Hardware
language:
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Author:

Qiu Lina¹^ORCID,Taft Rebecca²^ORCID,Shraer Alexander²^ORCID,Kollios George¹^ORCID

Affiliation:

1. Boston University, USA

2. Cockroach Labs, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3662010.3663440

Reference47 articles.

1. 2022. SWIOTLB performance optimizations. https://lore.kernel.org/lkml/20220630024238.GA884@gao-cwp/T

2. Performance Analysis of Scientific Computing Workloads on General Purpose TEEs

3. AMD. 2020. PostgreSQL on Google Compute Engine with N2D Confidential VMs. https://www.amd.com/system/files/documents/amd-epyc7002-gcpn2d-confidential-postgresql.pdf

4. AMD. 2021. Microsoft Azure Confidential Computing Powered by 3rd Gen EPYC™ CPUs. https://community.amd.com/t5/epyc-processors/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796

5. AMD. 2022. Google Cloud C2D VM Instances Powered by 3rd Gen EPYC. https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf

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