Abstract
In this paper, we provide a practical privacy-preserving generative model for time series data augmentation and sharing, called PART-GAN. Our model enables the local data curator to provide a freely accessible public generative model derived from original data for cloud storage. Compared with existing approaches, PART-GAN has three key advantages: It enables the generation of an unlimited amount of synthetic time series data under the guidance of a given classification label and addresses the incomplete and temporal irregularity issues. It provides a robust privacy guarantee that satisfies differential privacy to time series data augmentation and sharing. It addresses the trade-offs between utility and privacy by applying optimization strategies. We evaluate and report the utility and efficacy of PART-GAN through extensive empirical evaluations of real-world health/medical datasets. Even at a higher level of privacy protection, our method outperforms GAN with ordinary perturbation. It achieves similar performance with GAN without perturbation in terms of inception score, machine learning score similarity, and distance-based evaluations.
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References
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)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)
Arora, S., Ge, R., Neyshabur, B., Zhang, Y.: Stronger generalization bounds for deep nets via a compression approach. arXiv preprint arXiv:1802.05296 (2018)
Cynthia, D.: Differential privacy. In: Automata, Languages and Programming, pp. 1–12 (2006)
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our data, ourselves: privacy via distributed noise generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486–503. Springer, Heidelberg (2006). https://doi.org/10.1007/11761679_29
Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends® Theor. Comput. Sci. 9(3–4), 211–407 (2014)
Esteban, C., Hyland, S.L., Rätsch, G.: Real-valued (medical) time series generation with recurrent conditional GANs. arXiv preprint arXiv:1706.02633 (2017)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)
Koh, P.W., Liang, P.: Understanding black-box predictions via influence functions. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1885–1894. JMLR. org (2017)
Lee, N., Ajanthan, T., Torr, P.H.: SNIP: single-shot network pruning based on connection sensitivity. arXiv preprint arXiv:1810.02340 (2018)
Li, Y., Swersky, K., Zemel, R.: Generative moment matching networks. In: International Conference on Machine Learning, pp. 1718–1727 (2015)
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders. arXiv preprint arXiv:1511.05644 (2015)
Mescheder, L., Nowozin, S., Geiger, A.: Adversarial variational bayes: unifying variational autoencoders and generative adversarial networks. arXiv preprint arXiv:1701.04722 (2017)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 2642–2651. JMLR. org (2017)
Park, N., Mohammadi, M., Gorde, K., Jajodia, S., Park, H., Kim, Y.: Data synthesis based on generative adversarial networks. Proc. VLDB Endow. 11(10), 1071–1083 (2018)
Ramponi, G., Protopapas, P., Brambilla, M., Janssen, R.: T-CGAN: conditional generative adversarial network for data augmentation in noisy time series with irregular sampling. arXiv preprint arXiv:1811.08295 (2018)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)
Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for eeg decoding and visualization. Hum. Brain Mapp. 38(11), 5391–5420 (2017)
Song, S., Chaudhuri, K., Sarwate, A.D.: Stochastic gradient descent with differentially private updates. In: 2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 245–248. IEEE (2013)
Wang, Q., Zhang, Y., Lu, X., Wang, Z., Qin, Z., Ren, K.: Real-time and spatio-temporal crowd-sourced social network data publishing with differential privacy. IEEE Trans. Dependable Secure Comput. 15, 591–606 (2016)
Xie, L., Lin, K., Wang, S., Wang, F., Zhou, J.: Differentially private generative adversarial network. arXiv preprint arXiv:1802.06739 (2018)
Zhang, X., Ji, S., Wang, T.: Differentially private releasing via deep generative model. arXiv preprint arXiv:1801.01594 (2018)
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Wang, S., Rudolph, C., Nepal, S., Grobler, M., Chen, S. (2020). PART-GAN: Privacy-Preserving Time-Series Sharing. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_46
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