ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks-Reference-Cited by-同舟云学术

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ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Symposium on Security and Privacy (SP)
language:
Short-container-title:

Author:

Sun Zhichuang¹,Sun Ruimin²,Liu Changming³,Chowdhury Amrita Roy⁴,Lu Long³,Jha Somesh⁵

Affiliation:

1. Google

2. Florida International University

3. Northeastern University

4. University of California,San Diego

5. University of Wisconsin-Madison

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10179215/10179280/10179382.pdf?arnumber=10179382

Reference75 articles.

1. Knockoff Nets: Stealing Functionality of Black-Box Models

2. DarkneTZ

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1. Fault-tolerant deep learning inference on CPU-GPU integrated edge devices with TEEs;Future Generation Computer Systems;2024-12

2. DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference;IEEE Transactions on Mobile Computing;2024-10

3. Towards Protecting On-Device Machine Learning with RISC-V based Multi-Enclave TEE;2024 33rd International Conference on Computer Communications and Networks (ICCCN);2024-07-29

4. Tempo: Confidentiality Preservation in Cloud-Based Neural Network Training;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. CORELOCKER: Neuron-level Usage Control;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

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