SecNDP: Secure Near-Data Processing with Untrusted Memory
Wenjie Xiong, Liu Ke, Dimitrije Jankov, Michael Kounavis, Xiaochen Wang, Eric Northup, Jie Amy Yang, Bilge Acun, Carole-Jean Wu, Ping Tak Peter Tang, G. Edward Suh, Xuan Zhang, Hsien-Hsin S. Lee
摘要
Today's data-intensive applications increasingly suffer from significant performance bottlenecks due to the limited memory bandwidth of the classical von Neumann architecture. Near-Data Processing (NDP) has been proposed to perform computation near memory or data storage to reduce data movement for improving performance and energy consumption. However, the untrusted NDP processing units (PUs) bring in new threats to workloads that are private and sensitive, such as private database queries and private machine learning inferences. Meanwhile, most existing secure hardware designs do not consider off-chip components trustworthy. Once data leaving the processor, they must be protected, e.g., via block cipher encryption. Unfortunately, current encryption schemes do not support computation over encrypted data stored in memory or storage, hindering the adoption of NDP techniques for sensitive workloads.
In this paper, we propose SecNDP, a lightweight encryption and verification scheme for untrusted NDP devices to perform computation over ciphertext and verify the correctness of linear operations. Our encryption scheme leverages arithmetic secret sharing in secure Multi-Party Computation (MPC) to support operations over ciphertext, and uses counter-mode encryption to reduce the decryption latency. The security of the encryption and verification algorithm is formally proven. Compared with a non-NDP baseline, secure computation with SecNDP significantly reduces the memory bandwidth usage while providing security guarantees. We evaluate SecNDP for two workloads of distinct memory access patterns. In the setting of eight NDP units, we show a speedup up to 7.46× and energy savings of 18% over an unprotected non-NDP baseline, approaching the performance gain attained by native NDP without protection. Furthermore, SecNDP does not require any security assumption on NDP to hold, thus, using the same threat model as existing secure processors. SecNDP can be implemented without changing the NDP protocols and their inherent hardware design.
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引用它的顶会 Paper7
- Smart-Infinity: Fast Large Language Model Training using Near-Storage Processing on a Real SystemHongsun Jang, Jaeyong Song, Jaewon Jung, Jaeyoung Park 等HPCA 2024 · 被引用 26 次
- GPU-based Private Information Retrieval for On-Device Machine Learning InferenceMaximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng 等ASPLOS 2024 · 被引用 11 次
- SecureLoop: Design Space Exploration of Secure DNN AcceleratorsKyungmi Lee, Mengjia Yan, Joel S. Emer, Anantha P. ChandrakasanMICRO 2023 · 被引用 4 次
- RosenBridge: A Framework for Enabling Express I/O Paths Across the Virtualization BoundaryShi Qiu, Li Wang, Jianqin Yan, Ruofan Xiong 等FAST 2026 · 被引用 1 次
- Enabling Low-Cost Secure Computing on Untrusted In-Memory ArchitecturesSahar Ghoflsaz Ghinani, Jingyao Zhang, Elaheh SadrediniUSENIX Security 2025
它引用的顶会 Paper10
- Membership Inference Attacks Against Machine Learning ModelsReza Shokri, Marco Stronati, Congzheng Song, Vitaly ShmatikovS&P 2017 · 被引用 5,137 次
- ABY3: A Mixed Protocol Framework for Machine LearningPayman Mohassel, Peter RindalCCS 2018 · 被引用 898 次
- CrypTen: Secure Multi-Party Computation Meets Machine LearningBrian Knott, Shobha Venkataraman, Awni Y. Hannun, Shubho Sengupta 等NeurIPS 2021 · 被引用 573 次
- RecNMP: Accelerating Personalized Recommendation with Near-Memory ProcessingLiu Ke, Udit Gupta, Benjamin Youngjae Cho, David Brooks 等ISCA 2020 · 被引用 235 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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