Privacy-aware edge computing based on adaptive DNN partitioning

Chengshuai Shi, Lixing Chen, Cong Shen, Linqi Song, Jie Xu

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

Abstract

Recent years have witnessed deep neural networks (DNNs) become the de facto tool in many applications such as image classification and speech recognition. But significant unmet needs remain in performing DNN inference tasks on mobile devices. Although edge computing enables complex DNN inference tasks to be performed in close proximity to the mobile device, performance optimization requires a carefully designed synergy between the edge and the mobile device. Moreover, the confidentiality of uploaded data to the possibly untrusted edge server is of great concern. In this paper, we investigate the impact of DNN partitioning on the inference latency performance and the privacy risks in edge computing. Based on the obtained insights, we design an offloading strategy that adaptively partitions the DNN in varying network environments to make the optimal tradeoff between performance and privacy for battery-powered mobile devices. This strategy is designed under the learning-aided Lyapunov optimization framework and has a provable performance guarantee. Finally, we build a small- scale testbed to demonstrate the efficacy of the proposed offloading scheme.

Original languageEnglish (US)
Title of host publication2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728109626
DOIs
StatePublished - Dec 2019
Externally publishedYes
Event2019 IEEE Global Communications Conference, GLOBECOM 2019 - Waikoloa, United States
Duration: Dec 9 2019Dec 13 2019

Publication series

Name2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings

Conference

Conference2019 IEEE Global Communications Conference, GLOBECOM 2019
CountryUnited States
CityWaikoloa
Period12/9/1912/13/19

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Media Technology
  • Health Informatics

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  • Cite this

    Shi, C., Chen, L., Shen, C., Song, L., & Xu, J. (2019). Privacy-aware edge computing based on adaptive DNN partitioning. In 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings [9013742] (2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOBECOM38437.2019.9013742