Client-optimized algorithms and acceleration for encrypted compute offloading
McKenzie van der Hagen, Brandon Lucia
摘要
Homomorphic Encryption (HE) enables secure cloud offload processing on encrypted data. HE schemes are limited in the complexity and type of operations they can perform, motivating client-aided implementations that distribute computation between client (unencrypted) and server (encrypted). Prior client-aided systems optimize server performance, ignoring client costs: client-aided models put encryption and decryption on the critical path and require communicating large ciphertexts. We introduce Client-aided HE for Opaque Compute Offloading (CHOCO), a client-optimized system for encrypted offload processing. CHOCO reduces ciphertext size, reducing communication and computing costs through HE parameter minimization and through “rotational redundancy”, a new HE algorithm optimization. We present Client-aided HE for Opaque Compute Offloading Through Accelerated Cryptographic Operations (CHOCO-TACO), an accelerator for HE encryption and decryption, making client-aided HE feasible for even resource-constrained clients. CHOCO supports two popular HE schemes (BFV and CKKS) and several applications, including DNNs, PageRank, KNN, and K-Means. CHOCO reduces communication by up to 2948× over prior work. With CHOCO-TACO client enc-/decryption is up to 1094× faster and uses up to 648× less energy.
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引用它的顶会 Paper8
- CraterLake: a hardware accelerator for efficient unbounded computation on encrypted dataNikola Samardzic, Axel Feldmann, Aleksandar Krastev, Nathan Manohar 等ISCA 2022 · 被引用 205 次
- Orion: A Fully Homomorphic Encryption Framework for Deep LearningAustin Ebel, Karthik Garimella, Brandon ReagenASPLOS 2025 · 被引用 40 次
- HAAC: A Hardware-Software Co-Design to Accelerate Garbled CircuitsJianqiao Mo, Jayanth Gopinath, Brandon ReagenISCA 2023 · 被引用 23 次
- Characterizing and Optimizing End-to-End Systems for Private InferenceKarthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg 等ASPLOS 2023 · 被引用 15 次
- Cinnamon: A Framework for Scale-Out Encrypted AISiddharth Jayashankar, Edward Chen, Tom Tang, Wenting Zheng 等ASPLOS 2025 · 被引用 10 次
它引用的顶会 Paper11
- SecureML: A System for Scalable Privacy-Preserving Machine LearningPayman Mohassel, Yupeng ZhangS&P 2017 · 被引用 2,107 次
- GAZELLE: A Low Latency Framework for Secure Neural Network InferenceChiraag Juvekar, Vinod Vaikuntanathan, Anantha P. ChandrakasanUSENIX Security 2018 · 被引用 1,075 次
- Oblivious Neural Network Predictions via MiniONN TransformationsJian Liu, Mika Juuti, Yao Lu, N. AsokanCCS 2017 · 被引用 800 次
- XONN: XNOR-based Oblivious Deep Neural Network InferenceM. Sadegh Riazi, Mohammad Samragh, Hao Chen, Kim Laine 等USENIX Security 2019 · 被引用 314 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
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