Secure and Efficient Mobile DNN Using Trusted Execution Environments

Bin Hu, Yan Wang, Jerry Cheng, Tianming Zhao, Yucheng Xie, Xiaonan Guo, Yingying Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Many mobile applications have resorted to deep neural networks (DNNs) because of their strong inference capabilities. Since both input data and DNN architectures could be sensitive, there is an increasing demand for secure DNN execution on mobile devices. Towards this end, hardware-based trusted execution environments on mobile devices (mobile TEEs), such as ARM TrustZone, have recently been exploited to execute CNN securely. However, running entire DNNs on mobile TEEs is challenging as TEEs have stringent resource and performance constraints. In this work, we develop a novel mobile TEE-based security framework that can efficiently execute the entire DNN in a resource-constrained mobile TEE with minimal inference time overhead. Specifically, we propose a progressive pruning to gradually identify and remove the redundant neurons from a DNN while maintaining a high inference accuracy. Next, we develop a memory optimization method to deallocate the memory storage of the pruned neurons utilizing the low-level programming technique. Finally, we devise a novel adaptive partitioning method that divides the pruned model into multiple partitions according to the available memory in the mobile TEE and loads the partitions into the mobile TEE separately with a minimal loading time overhead. Our experiments with various DNNs and open-source datasets demonstrate that we can achieve 2-30 times less inference time with comparable accuracy compared to existing approaches securing entire DNNs with mobile TEE.

Original languageEnglish
Title of host publicationASIA CCS 2023 - Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Pages274-285
Number of pages12
ISBN (Electronic)9798400700989
DOIs
StatePublished - 10 Jul 2023
Event18th ACM ASIA Conference on Computer and Communications Security, ASIA CCS 2023 - Melbourne, Australia
Duration: 10 Jul 202314 Jul 2023

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Conference

Conference18th ACM ASIA Conference on Computer and Communications Security, ASIA CCS 2023
Country/TerritoryAustralia
CityMelbourne
Period10/07/2314/07/23

Keywords

  • DNN
  • Network Pruning
  • Security in Machine Learning
  • TEE

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