Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2020

Abstract

The proliferation of various mobile devices equipped with cameras results in an exponential growth of the amount of images. Recent advances in the deep learning with convolutional neural networks (CNN) have made CNN feature extraction become an effective way to process these images. However, it is still a challenging task to deploy the CNN model on the mobile sensors, which are typically resource-constrained in terms of the storage space, the computing capacity, and the battery life. Although cloud computing has become a popular solution, data security and response latency are always the key issues. Therefore, in this paper, we propose a novel lightweight framework for privacy-preserving CNN feature extraction for mobile sensing based on edge computing. To get the most out of the benefits of CNN with limited physical resources on the mobile sensors, we design a series of secure interaction protocols and utilize two edge servers to collaboratively perform the CNN feature extraction. The proposed scheme allows us to significantly reduce the latency and the overhead of the end devices while preserving privacy. Through theoretical analysis and empirical experiments, we demonstrate the security, effectiveness, and efficiency of our scheme.

Keywords

Mobile Sensing, Privacy-preserving, CNN, Feature extraction

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Dependable and Secure Computing

Volume

18

Issue

3

First Page

1441

Last Page

1455

ISSN

1545-5971

Identifier

10.1109/TDSC.2019.2913362

Publisher

IEEE Computer Society

Embargo Period

5-13-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TDSC.2019.2913362

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