Athena

Accelerating KeySwitch and Bootstrapping for Fully Homomorphic Encryption on CUDA GPU

Conference Paper (2026)
Author(s)

Yifan Yang (Huazhong University of Science and Technology)

Kexin Zhang (Huazhong University of Science and Technology)

Peng Xu (Huazhong University of Science and Technology)

Zhaojun Lu (Huazhong University of Science and Technology)

Wei Wang (Huazhong University of Science and Technology)

Weiqi Wang (Huazhong University of Science and Technology)

K. Liang (University of Turku, TU Delft - Cyber Security)

Research Group
Cyber Security
DOI related publication
https://doi.org/10.1007/978-3-032-07891-9_23
More Info
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Publication Year
2026
Language
English
Research Group
Cyber Security
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository as part of the Taverne amendment. More information about this copyright law amendment can be found at https://www.openaccess.nl. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
442-462
ISBN (print)
978-3-032-07890-2
ISBN (electronic)
978-3-032-07891-9
Reuse Rights

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Abstract

Fully Homomorphic Encryption (FHE) enables computation over encrypted data, but it faces significant challenges in practical implementation due to its high computational costs, particularly in HMult, HRot, and Bootstrapping operations. This work presents Athena, an accelerated FHE system built on GPUs with a new algorithm-hardware co-design approach. Specifically, to accelerate HMult, HRot, and Bootstrapping, we redesign their common and expensive operation KeySwitch, based on the KLSS method proposed by Kim et al. in CRYPTO’23, and accelerate its core operations, namely NTT, EBConv, and IP. We further optimize the dataflow of Bootstrapping by reducing redundant EBConv and (I)NTT operations, and by improving the global memory I/O in the double-hoisting-based C2S/S2C operation. Moreover, Athena is designed as a general-purpose system that supports various cryptographic parameters. Experimental results demonstrate that Athena significantly improves the performance of KeySwitch and Bootstrapping. In particular, Athena’s accelerated KeySwitch optimizes HMult 2.17\times \sim 4.40\times and HRot 1.89\times \sim 4.54\times compared to TensorFHE (HPCA’23), Poseidon (HPCA’23), and FAB (HPCA’23), respectively. Besides, Athena’s Bootstrapping outperforms TensorFHE by nearly 2.74\times .

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