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Performance of hierarchical transforms in homomorphic encryption: a case study on logistic regression inference

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Abstract

Recent works challenged the number-theoretic transform (NTT) as the most efficient method for polynomial multiplication in GPU implementations of fully homomorphic encryption schemes such as CKKS and BFV. In particular, these works argue that the discrete galois transform (DGT) is a better candidate for this particular case. However, these claims were never rigorously validated, and only intuition was used to argue in favor of each transform. This work brings some light on the discussion by developing similar CUDA implementations of the CKKS cryptosystem, differing only in the underlying transform and related data structure. We ran several experiments and collected performance metrics in different contexts, ranging from the basic direct comparison between the transforms to measuring the impact of each one on the inference phase of the logistic regression algorithm. Our observations suggest that, despite some specific polynomial ring configurations, the DGT in a standalone implementation does not offer the same performances of the NTT. However, when we consider the entire cryptosystem, we noticed that the effects of the higher arithmetic density of the DGT on other parts of the implementation are substantial, implying a considerable performance improvement of up to \(15\%\) on the homomorphic multiplication. Furthermore, this speedup is consistent when we consider a more complex application, indicating that the DGT suits better the target architecture.

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Acknowledgements

This work was supported in part by the Brazilian National Council for Scientific and Technological Development (CNPq), grants number 164489/2018-5 and 203175/2019-0; and the Brazilian Coordination for the Improvement of Higher Education Personnel Foundation (CAPES) grant number 1591123. We specially thank Google for GCP Research Credits Program under number 106101194491; the Concordium Blockchain Research Center at Aarhus University (COBRA), Denmark; and the European Research Council (ERC) under the European Unions’s Horizon 2020 research and innovation programme under grant agreement No. 803096 (SPEC).

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  1. Institute of Computing, University of Campinas, Campinas, Brazil

    Pedro Geraldo M. R. Alves & Jheyne N. Ortiz

  2. Department of Computer Science, Aarhus University, Aarhus, Denmark

    Pedro Geraldo M. R. Alves & Diego F. Aranha

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Alves, P.G.M.R., Ortiz, J.N. & Aranha, D.F. Performance of hierarchical transforms in homomorphic encryption: a case study on logistic regression inference. J Cryptogr Eng 13, 295–310 (2023). https://doi.org/10.1007/s13389-023-00325-1

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