PEGASUS: Bridging Polynomial and Non-polynomial Evaluations in Homomorphic Encryption
Wen-jie Lu, Zhicong Huang, Cheng Hong, Yiping Ma, Hunter Qu
摘要
Homomorphic encryption (HE) is considered as one of the most important primitives for privacy-preserving applications. However, an efficient approach to evaluate both polynomial and non-polynomial functions on encrypted data is still absent, which hinders the deployment of HE to real-life applications. To address this issue, we propose a practical framework PEGASUS. PEGASUS can efficiently switch back and forth between a packed CKKS ciphertext and FHEW ciphertexts without decryption, allowing us to evaluate arithmetic functions efficiently on the CKKS side, and to evaluate look-up tables on FHEW ciphertexts. Our FHEW → CKKS conversion algorithm is more practical than the existing methods. We improve the computational complexity from linear to sublinear. Moreover, the size of our conversion key is significantly smaller, e.g., reduced from 80 gigabytes to 12 megabytes. We present extensive benchmarks of PEGASUS, including sigmoid/ReLU/min/max/division, sorting and max-pooling. To further demonstrate the capability of PEGASUS, we developed two more applications. The first one is a private decision tree evaluation whose communication cost is about two orders of magnitude smaller than the previous HE-based approaches. The second one is a secure K-means clustering that is able to run on thousands of encrypted samples in minutes that outperforms the best existing system by 14 × -20×. To the best of our knowledge, this is the first work that supports practical K-means clustering using HE in a single server setting.
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引用它的顶会 Paper23
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- MOAI: Module-Optimizing Architecture for Non-Interactive Secure Transformer InferenceLinru Zhang, Xiangning Wang, Sim Jun Jie, Zhicong Huang 等ICLR 2026 · 被引用 24 次
- "Get in Researchers; We're Measuring Reproducibility": A Reproducibility Study of Machine Learning Papers in Tier 1 Security ConferencesDaniel Olszewski, Allison Lu, Carson Stillman, Kevin Warren 等CCS 2023 · 被引用 19 次
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它引用的顶会 Paper6
- GAZELLE: A Low Latency Framework for Secure Neural Network InferenceChiraag Juvekar, Vinod Vaikuntanathan, Anantha P. ChandrakasanUSENIX Security 2018 · 被引用 1,075 次
- Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network InferenceHao Chen, Wei Dai, Miran Kim, Yongsoo SongCCS 2019 · 被引用 235 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
- On the Security of Homomorphic Encryption on Approximate NumbersBaiyu Li, Daniele MicciancioEUROCRYPT 2021 · 被引用 165 次
- EVA: an encrypted vector arithmetic language and compiler for efficient homomorphic computationRoshan Dathathri, Blagovesta Kostova, Olli Saarikivi, Wei Dai 等PLDI 2020 · 被引用 117 次
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