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.
Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDC02040400.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tankard, C.: What the GDPR means for businesses. Netw. Secur. 2016(6), 5–8 (2016)
Konečný, J., Mcmahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency (2016)
Melis, L., Song, C., De Cristofaro, E., Shmatikov, V.: Exploiting unintended feature leakage in collaborative learning. IEEE (2019)
Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1322–1333. ACM (2015)
Hitaj, B., Ateniese, G., Pérez-Cruz, F.: Deep models under the GAN: information leakage from collaborative deep learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 603–618. ACM (2017)
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., Seth, K.: Practical secure aggregation for privacy-preserving machine learning. In: ACM SIGSAC Conference on Computer & Communications Security (2017)
Le, T.P., Aono, Y., Hayashi, T., Wang, L., Moriai, S.: Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans. Inf. Forensics Secur. PP(99), 1 (2017)
Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310–1321. ACM (2015)
Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308–318. ACM (2016)
Zhang, T., He, Z., Lee, R.B.: Privacy-preserving machine learning through data obfuscation (2018)
Sharma, S., Chen, K.: Poster: image disguising for privacy-preserving deep learning (2019)
Triastcyn, A., Faltings, B.: Generating artificial data for private deep learning (2018)
Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation (2017)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE International Conference on Computer Vision (2017)
Sim, T., Zhang, L.: Controllable face privacy. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 4, pp. 1–8. IEEE (2015)
Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition (2008)
Liu, Z., Luo, P., Wang, X., Tang, X.: Large-scale celebfaces attributes (celeba) dataset (2018). Accessed 15 Aug 2018
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hou, W., Wang, D., Chen, X. (2020). Generate Images with Obfuscated Attributes for Private Image Classification. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-37734-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37733-5
Online ISBN: 978-3-030-37734-2
eBook Packages: Computer ScienceComputer Science (R0)