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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Generate Images with Obfuscated Attributes for Private Image Classification
verfasst von : Wei Hou, Dakui Wang, Xiaojun Chen
Erschienen in: MultiMedia Modeling
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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.