Lightweight privacy-preserving GAN framework for model training and image synthesis

Publication Type

Journal Article

Publication Date

1-2022

Abstract

Generative adversarial network (GAN) has excellent performance for data generation and is widely used in image synthesis. Outsourcing GAN to cloud platform is a popular way to save local computation resources and improve the efficiency, but it still faces the privacy leakage concerns: (1) the sensitive information of the training dataset may be disclosed in the cloud; (2) the trained model may reveal the privacy of training samples since it extracts the characteristics from the data. In this paper, we propose a lightweight privacy-preserving GAN framework (LP-GAN) for model training and image synthesis based on secret sharing scheme. Specifically, we design a series of efficient secure interactive protocols for different layers (convolution, batch normalization, ReLU, Sigmoid) of neural network (NN) used in GAN. Our protocols are scalable to build secure training or inference tasks for NN-based applications. We utilize edge computing to reduce the latency and all the protocols are executed on two edge servers collaboratively. Compared with the existing schemes, the proposed solution greatly improves efficiency, reduces communication overhead, and guarantees the privacy. We prove the correctness and security of LP-GAN by theoretical analysis. Extensive experiments on different real-world datasets demonstrate the effectiveness, accuracy, and efficiency of our scheme.

Keywords

Protocols, Generative adversarial networks, Training, Cryptography, Computational modeling, Image synthesis, Privacy, Privacy-preserving, generative adversarial network, secret sharing, secure computation, deep learning

Discipline

Information Security

Research Areas

Cybersecurity; Information Systems and Management

Publication

IEEE Transactions on Information Forensics and Security

Volume

17

First Page

1083

Last Page

1098

ISSN

1556-6013

Identifier

10.1109/TIFS.2022.3156818

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TIFS.2022.3156818

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