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TeeBench: Seamless Benchmarking in Trusted Execution Environments

Published: 05 June 2023 Publication History

Abstract

Trusted Execution Environments (TEEs) have enabled building secure systems that operate on untrusted machines. However, TEEs' architecture questions previous performance findings. The existing relational algorithms have been designed for traditional CPUs. Prior work has shown that these algorithms underperform in TEEs and, in most cases, can not be easily reused. Moreover, they frequently used benchmarks pertinent to CPUs and ignored TEE-specific metrics essential to understand the performance differences. Therefore, there is a need for a fair benchmarking approach for TEE algorithms.
In this demonstration, we showcase TeeBench, a unified benchmarking framework for relational operators across TEEs. TeeBench focuses on TEE-specific hardware metrics. It enables a comprehensive performance analysis that helps researchers to evaluate their advances. It comes with an interactive web browser tool that allows the users to upload their implementation of a relational algorithm and seamlessly benchmark it across different TEEs. In addition, it introduces a novel TEE-Analyzer that hints the users about performance bottlenecks and suggests possible code improvements. Users receive instant feedback if changes to their algorithm improve the performance through an interactive, human-friendly web interface. We expect TeeBench to encourage the usage of TEEs and to advance the study of privacy-preserving systems.

Supplemental Material

MP4 File
TeeBench assists database engineers in improving the performance of relational operators in TEEs. Check out the video for more details!

References

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Panagiotis Antonopoulos, Arvind Arasu, Kunal D Singh, Ken Eguro, Nitish Gupta, Rajat Jain, Raghav Kaushik, Hanuma Kodavalla, Donald Kossmann, Nikolas Ogg, et al. 2020. Azure SQL Database Always Encrypted. In SIGMOD.
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Cagri Balkesen, Jens Teubner, Gustavo Alonso, and M Tamer Özsu. 2013. Main-memory hash joins on multi-core CPUs: Tuning to the underlying hardware. In ICDE.
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Deloitte. 2020. Cloud banking: More than just a CIO conversation. What will financial services of the future look like with cloud? https://www2.deloitte.com/za/en/pages/ financial-services/articles/bank-2030-financial-services-cloud.html
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Github. 2021. Intel Performance Counter Monitor. https://github.com/intel/PCM
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Kajetan Maliszewski. 2020. Secure Data Processing at Scale. Proceedings of the VLDB PhD Workshop (2020).
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Kajetan Maliszewski, Jorge-Arnulfo Quiané-Ruiz, Jonas Traub, and Volker Markl. 2021. What is the price for joining securely? benchmarking equi-joins in trusted execution environments. PVLDB (2021).
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Wenting Zheng, Ankur Dave, Jethro G Beekman, Raluca Ada Popa, Joseph E Gonzalez, and Ion Stoica. 2017. Opaque: an oblivious and encrypted distributed analytics platform. In NSDI.

Cited By

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  • (2023)Cracking-Like Join for Trusted Execution EnvironmentsProceedings of the VLDB Endowment10.14778/3598581.359860216:9(2330-2343)Online publication date: 1-May-2023

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  1. TeeBench: Seamless Benchmarking in Trusted Execution Environments

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    cover image ACM Conferences
    SIGMOD '23: Companion of the 2023 International Conference on Management of Data
    June 2023
    330 pages
    ISBN:9781450395076
    DOI:10.1145/3555041
    Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    New York, NY, United States

    Publication History

    Published: 05 June 2023

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    Author Tags

    1. benchmarking
    2. database
    3. trusted execution environments

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    TeeBench assists database engineers in improving the performance of relational operators in TEEs. Check out the video for more details! https://dl.acm.org/doi/10.1145/3555041.3589726#movie.mp4

    Funding Sources

    • BIFOLD - Berlin Institute for the Foundations of Learning and Data

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    SIGMOD/PODS '23
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    Overall Acceptance Rate 785 of 4,003 submissions, 20%

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    • (2023)Cracking-Like Join for Trusted Execution EnvironmentsProceedings of the VLDB Endowment10.14778/3598581.359860216:9(2330-2343)Online publication date: 1-May-2023

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