Abstract
Deep learning has been increasingly applied in a wide variety of domains and has achieved remarkable results. Generally, a large amount of data is needed to train a deep learning model. The data might contain sensitive information, leading to the risk of privacy leakage in the process of model training and model application. As a privacy definition with strict mathematical guarantee, differential privacy has gained great attention and has been widely studied in recent years. However, applying differential privacy to deep learning still faces a big challenge to reduce the impact on model accuracy. In this paper, we first analyze the privacy threats that exist in deep learning from the perspective of privacy attacks, including membership inference attacks and reconstruction attacks. We also introduce some basic theories necessary to apply differential privacy to deep learning. Second, to summarize how existing works apply differential privacy to deep learning, we divide perturbations into four categories based on different perturbation stages of deep learning with differential privacy: input perturbation, parameter perturbation, objective function perturbation, and output perturbation. Finally, the challenges and future research directions on deep learning with differential privacy are summarized.
This work was supported in part by the National Natural Science Foundation of China under Grant 61672106, and in part by the Natural Science Foundation of Beijing, China under Grant L192023 and in part by the project of Scientific research fund of Beijing Information Science and Technology University of under 5029923412.
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Zhang, Y., Cai, Y., Zhang, M., Li, X., Fan, Y. (2022). A Survey on Privacy-Preserving Deep Learning with Differential Privacy. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_2
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