BTS: an accelerator for bootstrappable fully homomorphic encryption
Sangpyo Kim, Jongmin Kim, Michael Jaemin Kim, Wonkyung Jung, John Kim, Minsoo Rhu, Jung Ho Ahn
摘要
Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by providing computation on encrypted data (ciphertexts). HE is based on noisy encryption schemes in which noise accumulates as more computations are applied to the data. The limited number of operations applicable to the data prevents practical applications from exploiting HE. Bootstrapping enables an unlimited number of operations or fully HE (FHE) by refreshing the ciphertext. Unfortunately, bootstrapping requires a significant amount of additional computation and memory bandwidth as well. Prior works have proposed hardware accelerators for computation primitives of FHE. However, to the best of our knowledge, this is the first to propose a hardware FHE accelerator that supports bootstrapping as a first-class citizen.
In particular, we propose BTS -Bootstrappable, Technologydriven, Secure accelerator architecture for FHE. We identify the challenges of supporting bootstrapping in the accelerator and analyze the off-chip memory bandwidth and computation required. In particular, given the limitations of modern memory technology, we identify the HE parameter sets that are efficient for FHE acceleration. Based on the insights gained from our analysis, we propose BTS, which effectively exploits the parallelism innate in HE operations by arranging a massive number of processing elements in a grid. We present the design and microarchitecture of BTS, including a network-on-chip design that exploits a deterministic communication pattern. BTS shows 5,556× and 1,306× improved execution time on ResNet-20 and logistic regression over a CPU, with a chip area of 373.6mm 2 and up to 163.2W of power.
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引用它的顶会 Paper40
- ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key ReuseJongmin Kim, Gwangho Lee, Sangpyo Kim, Gina Sohn 等MICRO 2022 · 被引用 160 次
- BOLT: Privacy-Preserving, Accurate and Efficient Inference for TransformersQi Pang, Jinhao Zhu, Helen Möllering, Wenting Zheng 等S&P 2024 · 被引用 149 次
- FAB: An FPGA-based Accelerator for Bootstrappable Fully Homomorphic EncryptionRashmi Agrawal, Leo de Castro, Guowei Yang, Chiraag Juvekar 等HPCA 2023 · 被引用 136 次
- Poseidon: Practical Homomorphic Encryption AcceleratorYinghao Yang, Huaizhi Zhang, Shengyu Fan, Hang Lu 等HPCA 2023 · 被引用 109 次
- TensorFHE: Achieving Practical Computation on Encrypted Data Using GPGPUShengyu Fan, Zhiwei Wang, Weizhi Xu, Rui Hou 等HPCA 2023 · 被引用 90 次
它引用的顶会 Paper9
- GAZELLE: A Low Latency Framework for Secure Neural Network InferenceChiraag Juvekar, Vinod Vaikuntanathan, Anantha P. ChandrakasanUSENIX Security 2018 · 被引用 1,075 次
- F1: A Fast and Programmable Accelerator for Fully Homomorphic EncryptionNikola Samardzic, Axel Feldmann, Aleksandar Krastev, Srinivas Devadas 等MICRO 2021 · 被引用 294 次
- HEAX: An Architecture for Computing on Encrypted DataM. Sadegh Riazi, Kim Laine, Blake Pelton, Wei DaiASPLOS 2020 · 被引用 244 次
- Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse KeysJean-Philippe Bossuat, Christian Mouchet, Juan Ramón Troncoso-Pastoriza, Jean-Pierre HubauxEUROCRYPT 2021 · 被引用 179 次
- Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private InferenceBrandon Reagen, Wooseok Choi, Yeongil Ko, Vincent T. Lee 等HPCA 2021 · 被引用 147 次
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