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TeeBench: Seamless Benchmarking in Trusted Execution Environments

Published:05 June 2023Publication 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.

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References

  1. 2016. Regulation (EU) 2016/679 of the european parliament and of the council of 27 April 2016. Official Journal of the European Union (2016).Google ScholarGoogle Scholar
  2. 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.Google ScholarGoogle Scholar
  3. 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.Google ScholarGoogle Scholar
  4. 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.htmlGoogle ScholarGoogle Scholar
  5. European Banking Federation. 2020. The use of Cloud Computing by Financial Institutions. Technical Report. Brussels, BE.Google ScholarGoogle Scholar
  6. Github. 2021. Intel Performance Counter Monitor. https://github.com/intel/PCMGoogle ScholarGoogle Scholar
  7. Kajetan Maliszewski. 2020. Secure Data Processing at Scale. Proceedings of the VLDB PhD Workshop (2020).Google ScholarGoogle Scholar
  8. 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).Google ScholarGoogle Scholar
  9. 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.Google ScholarGoogle Scholar

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    • Published in

      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

      Copyright © 2023 ACM

      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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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 5 June 2023

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