Publication Type

Journal Article

Version

acceptedVersion

Publication Date

11-2019

Abstract

Benefiting from the advancement of algorithms in massive data and powerful computing resources, deep learning has been explored in a wide variety of fields and produced unparalleled performance results. It plays a vital role in daily applications and is also subtly changing the rules, habits, and behaviors of society. However, inevitably, data-based learning strategies are bound to cause potential security and privacy threats, and arouse public as well as government concerns about its promotion to the real world. In this article, we mainly focus on data security issues in deep learning. We first investigate the potential threats of deep learning in this area, and then present the latest countermeasures based on various underlying technologies, where the challenges and research opportunities on offense and defense are also discussed. Then, we propose SecureNet, the first verifiable and privacy-preserving prediction protocol to protect model integrity and user privacy in DNNs. It can significantly resist various security and privacy threats during the prediction process. We simulate SecureNet under a real dataset, and the experimental results show the superior performance of SecureNet for detecting various integrity attacks against DNN models.

Keywords

Integrity attacks, Learning strategy, Potential threats, Prediction process, Privacy preserving

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Communications Magazine

Volume

57

Issue

11

First Page

116

Last Page

122

ISSN

0163-6804

Identifier

10.1109/MCOM.001.1900091

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/MCOM.001.1900091

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