research-article

Compiler-Based Memory Encryption for Machine Learning on Commodity Low-Power Devices

Authors:
Kiwan Maeng

Pennsylvania State University, University Park, USA

Pennsylvania State University, University Park, USA

0000-0002-0321-8406
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,
Brandon Lucia

Carnegie Mellon University, Pittsburgh, USA

Carnegie Mellon University, Pittsburgh, USA

0000-0003-4130-1099
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CC 2024: Proceedings of the 33rd ACM SIGPLAN International Conference on Compiler ConstructionFebruary 2024Pages 198–211https://doi.org/10.1145/3640537.3641564

Published:20 February 2024Publication History

CC 2024: Proceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction

Pages 198–211

ABSTRACT

Running machine learning (ML) on low-power IoT devices exposes unique security concerns. Attackers can easily steal or manipulate sensitive user data or proprietary ML models from the devices’ off-chip memory by leveraging their simple hardware structure and the lack of memory encryption hardware. To protect against these real-world threats, we propose a lightweight compiler-based memory encryption scheme, Spitz. Spitz achieves full off-chip memory encryption only with common architectural components on commodity devices, such as programmable on-chip SRAM, AES hardware, and Direct-Memory Access (DMA). Our evaluation on real hardware shows that Spitz maintains competitive performance while realizing full off-chip memory encryption. Spitz is only 1.16–1.73× slower than our best-effort non-secure baseline, and is even faster by 1.5–2.23× compared to a non-secure popular vendor library.

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Index Terms

Compiler-Based Memory Encryption for Machine Learning on Commodity Low-Power Devices
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
      1. Embedded software
2. Security and privacy
  1. Systems security

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Published in
CC 2024: Proceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction
February 2024
261 pages
ISBN:9798400705076
DOI:10.1145/3640537
General Chair:
Gabriel Rodríguez
Universidade da Coruña, Spain
,
Program Chairs:
P. Sadayappan
University of Utah, USA
,
Aravind Sukumaran-Rajam
Meta, USA
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- Published: 20 February 2024
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Compiler-Based Memory Encryption for Machine Learning on Commodity Low-Power Devices

CC 2024: Proceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction

ABSTRACT

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