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
Citation
CHEN, Zhenzhu; WU, Jie; FU, Anmin; SU, Mang; and DENG, Robert H..
MP-CLF: An effective model-preserving collaborative deep learning framework for mitigating data leakage under the GAN. (2023). Knowledge-Based Systems. 270, (C), 1-13.
Available at: https://ink.library.smu.edu.sg/sis_research/8556
Additional URL
https://doi.org/10.1016/j.knosys.2023.110527