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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE International Symposium on High-Performance Computer Architecture, HPCA 2022
PublisherIEEE Computer Society
Pages244-258
Number of pages15
ISBN (Electronic)9781665420273
DOIs
StatePublished - 2022
Externally publishedYes
Event28th Annual IEEE International Symposium on High-Performance Computer Architecture, HPCA 2022 - Virtual, Online, Korea, Republic of
Duration: Apr 2 2022Apr 6 2022

Publication series

NameProceedings - International Symposium on High-Performance Computer Architecture
Volume2022-April
ISSN (Print)1530-0897

Conference

Conference28th Annual IEEE International Symposium on High-Performance Computer Architecture, HPCA 2022
Country/TerritoryKorea, Republic of
CityVirtual, Online
Period4/2/224/6/22

Keywords

  • Cryptography
  • Near-Data Processing
  • Privacy-Preserving Machine Learning
  • Security and Privacy

ASJC Scopus subject areas

  • Hardware and Architecture

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