MP-CLF: An effective model-preserving collaborative deep learning framework for mitigating data leakage under the GAN

Publication Type

Journal Article

Publication Date

6-2023

Abstract

The development of Internet of Things (IoT) communication technology has accelerated the data transmission between IoT devices, thus facilitating collaborative data processing based on the cloud, such as collaborative deep learning. The collaborative deep learning framework allows local devices to cooperate on training models without sharing private data, which resolves the contradiction of the availability and privacy of data. However, the emergence of the Generative Adversarial Network (GAN) attack has shown that poorly protected local data is vulnerable to being learned by adversaries. In this paper, we aim to address the threat GAN attacks pose to collaborative deep learning. We propose a Model-Preserving Collaborative deep Learning Framework, called MP-CLF, which can effectively resist the GAN attack. Based on fully connected neural network learning, MP-CLF employs a matrix blinding technology to break the local modeling of the GAN attack by blinding specific model parameters and trainers’ data, which is easily implementable and has strong security. Besides, MP-CLF builds a user partition model pre-training to improve training quality and strengthen model protection. Using the MNIST dataset and Fashion-MNIST dataset, we experimentally demonstrate that MP-CLF can completely resist the GAN attack with good computational efficiency

Keywords

Attack resistance, Blinding, Collaborative deep learning, GAN attack, Model privacy

Discipline

Information Security

Research Areas

Cybersecurity

Publication

Knowledge-Based Systems

Volume

270

Issue

C

First Page

1

Last Page

13

ISSN

0950-7051

Identifier

10.1016/j.knosys.2023.110527

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.knosys.2023.110527

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