A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Athena: Accelerating KeySwitch and Bootstrapping for Fully Homomorphic Encryption on CUDA GPU
Tekijät: Yang, Yifan; Zhang, Kexin; Xu, Peng; Lu, Zhaojun; Wang, Wei; Wang, Weiqi; Liang, Kaitai
Toimittaja: Nicomette, Vincent; Benzekri, Abdelmalek; Boulahia-Cuppens, Nora; Vaidya, Jaideep
Konferenssin vakiintunut nimi: European Symposium on Research in Computer Security
Kustantaja: Springer Science and Business Media Deutschland GmbH
Julkaisuvuosi: 2025
Lehti: Lecture Notes in Computer Science
Kokoomateoksen nimi: Computer Security – ESORICS 2025 : 30th European Symposium on Research in Computer Security, Toulouse, France, September 22–24, 2025, Proceedings, Part II
Vuosikerta: 16054
Aloitussivu: 442
Lopetussivu: 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
Julkaisun avoimuus kirjaamishetkellä: Ei avoimesti saatavilla
Julkaisukanavan avoimuus : Ei avoin julkaisukanava
Verkko-osoite: 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×.
Julkaisussa olevat rahoitustiedot:
This work was supported by National Key Research and Development Program of China (Grant No. 2022YFB4501500 and 2022YFB4501502).
