A4 Refereed article in a conference publication
Athena: Accelerating KeySwitch and Bootstrapping for Fully Homomorphic Encryption on CUDA GPU
Authors: Yang, Yifan; Zhang, Kexin; Xu, Peng; Lu, Zhaojun; Wang, Wei; Wang, Weiqi; Liang, Kaitai
Editors: Nicomette, Vincent; Benzekri, Abdelmalek; Boulahia-Cuppens, Nora; Vaidya, Jaideep
Conference name: European Symposium on Research in Computer Security
Publisher: Springer Science and Business Media Deutschland GmbH
Publication year: 2025
Journal: Lecture Notes in Computer Science
Book title : Computer Security – ESORICS 2025 : 30th European Symposium on Research in Computer Security, Toulouse, France, September 22–24, 2025, Proceedings, Part II
Volume: 16054
First page : 442
Last page: 462
ISBN: 978-3-032-07890-2
eISBN: 978-3-032-07891-9
ISSN: 0302-9743
eISSN: 1611-3349
DOI: https://doi.org/10.1007/978-3-032-07891-9_23
Publication's open availability at the time of reporting: No Open Access
Publication channel's open availability : No Open Access publication channel
Web address : https://link.springer.com/chapter/10.1007/978-3-032-07891-9_23
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 HMult2.17×∼4.40× and HRot1.89×∼4.54× compared to TensorFHE (HPCA’23), Poseidon (HPCA’23), and FAB (HPCA’23), respectively. Besides, Athena’s Bootstrapping outperforms TensorFHE by nearly 2.74×.
Funding information in the publication:
This work was supported by National Key Research and Development Program of China (Grant No. 2022YFB4501500 and 2022YFB4501502).
