research-article

DarkneTZ: towards model privacy at the edge using trusted execution environments

Authors:

Ali Shahin Shamsabadi,

Kleomenis Katevas,

Soteris Demetriou,

Ilias Leontiadis,

Andrea Cavallaro,

Hamed HaddadiAuthors Info & Claims

MobiSys '20: Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services

Pages 161 - 174

https://doi.org/10.1145/3386901.3388946

Published: 15 June 2020 Publication History

Abstract

We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neural Networks (DNNs). Increasingly, edge devices (smartphones and consumer IoT devices) are equipped with pre-trained DNNs for a variety of applications. This trend comes with privacy risks as models can leak information about their training data through effective membership inference attacks (MIAs).

We evaluate the performance of DarkneTZ, including CPU execution time, memory usage, and accurate power consumption, using two small and six large image classification models. Due to the limited memory of the edge device's TEE, we partition model layers into more sensitive layers (to be executed inside the device TEE), and a set of layers to be executed in the untrusted part of the operating system. Our results show that even if a single layer is hidden, we can provide reliable model privacy and defend against state of the art MIAs, with only 3% performance overhead. When fully utilizing the TEE, DarkneTZ provides model protections with up to 10% overhead.

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Index Terms

DarkneTZ: towards model privacy at the edge using trusted execution environments
1. Computing methodologies
  1. Machine learning
2. Security and privacy
  1. Security in hardware
    1. Embedded systems security

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cover image ACM Conferences

MobiSys '20: Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services

June 2020

496 pages

ISBN:9781450379540

DOI:10.1145/3386901

General Chairs:
Eyal de Lara
University of Toronto
,
Iqbal Mohomed
Samsung AI Centre Toronto
,
Program Chairs:
Jason Nieh
Columbia University
,
Elizabeth M. Belding
UC Santa Barbara

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Published: 15 June 2020

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MobiSys '20: The 18th Annual International Conference on Mobile Systems, Applications, and Services

June 15 - 19, 2020

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Zobaed SAmini Salehi M(2025)Confidential Computing Across Edge‐To‐Cloud for Machine Learning: A Survey StudySoftware: Practice and Experience10.1002/spe.3398Online publication date: 3-Jan-2025
https://doi.org/10.1002/spe.3398
Bruhns IBerndt SSander JEisenbarth T(2024)Slalom at the Carnival: Privacy-preserving Inference with Masks from Public KnowledgeIACR Communications in Cryptology10.62056/akp-49qgxqOnline publication date: 7-Oct-2024
https://doi.org/10.62056/akp-49qgxq
Zhang ZWang NZhang ZZhang YZhang TLiu JWu YSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)GROUPCOVERProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694550(59992-60003)Online publication date: 21-Jul-2024
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