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POPS: an off-peak precomputing scheme for privacy-preserving computing

Abstract

Emerging privacy-preserving technologies help protect sensitive data during application executions. Recently, the secure two-party computing (TPC) scheme has demonstrated its potential, especially for the secure model inference of a deep learning application by protecting both the user input data and the model parameters. Nevertheless, existing TPC protocols incur excessive communications during the program execution, which lengthens the execution time. In this work, we propose the precomputing scheme, POPS, to address the problem, which is done by shifting the required communications from during the execution to the time prior to the execution. Particular, the multiplication triple generation is computed beforehand with POPS to remove the overhead at runtime. We have analyzed the TPC protocols to ensure that the precomputing scheme conforms the existing secure protocols. Our results show that POPS takes a step forward in the secure inference by delivering up to \(20\times \) and \(5\times \) speedups against the prior work for the microbenchmark and the convolutional neural network experiments, respectively.

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Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Notes

  1. Note that there is no communication during the online phase if the protocol is the Yao sharing.

  2. The C-OT\(^{2\ell }_\ell \) is used for Arithmetic sharing, which has higher communication bandwidth than R-OT. Boolean MT is generated by R-OT.

  3. We assume that Yao’s and Arithmetic sharing protocols are adopted in the TPC program and the rationale is provided in Sect. 4.2.

  4. There has been an implementation for Boolean sharing in ABY, but the MT counting method of Boolean sharing is different from our proposed version (for Arithmetic sharing) due to the support for different protocols.

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Acknowledgments

This work is financially supported by Industrial Technology Research Institute, Taiwan. This work is supported in part by the Ministry of Science and Technology, Taiwan, under the grant number MOST 110-2221-E-006-052. This work is financially supported by the Intelligent Manufacturing Research Center (iMRC) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE).

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Huang, PH., Chang, TW., Tu, CH. et al. POPS: an off-peak precomputing scheme for privacy-preserving computing. J Supercomput (2022). https://doi.org/10.1007/s11227-022-04552-x

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Keywords

  • Privacy-preserving computation
  • Secure two-party computation
  • Multiplication triple
  • ABY
  • Deep learning models
  • Convolutional neural networks