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Multi-scale Comparison Network for Few-Shot Learning Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Generate Images with Obfuscated Attributes for Private Image Classification

Authors : Wei Hou, Dakui Wang, Xiaojun Chen

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

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Abstract

Image classification is widely used in various applications and some companies collect a large amount of data from users to train classification models for commercial profitability. To prevent disclosure of private information caused by direct data collecting, Google proposed federated learning to share model parameters rather than data. However, this framework could address the problem of direct data leakage but cannot defend against inference attack, malicious participants can still exploit attribute information from the model parameters.
In this paper, we propose a novel method based on StarGAN to generate images with obfuscated attributes. The images generated by our methods can retain the non-private attributes of the original image but protect the specific private attributes of the original image by mixing the original image and the artificial image with obfuscated attributes. Experimental results have shown that the model trained on the artificial image dataset can effectively defend against property inference attack with neglected accuracy loss of classification task in a federated learning environment.

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Metadata
Title
Generate Images with Obfuscated Attributes for Private Image Classification
Authors
Wei Hou
Dakui Wang
Xiaojun Chen
Copyright Year
2020
Book
MultiMedia Modeling

Print ISBN: 978-3-030-37733-5

Electronic ISBN: 978-3-030-37734-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-37734-2_11