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A Survey of Privacy Preservation for Deep Learning Applications

Ling Zhang1,*, Lina Nie1, Leyan Yu2

1 School of Computer Science, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 Reading Academy, Nanjing University of Information Science & Technology, Nanjing, 210044, China

* Corresponding Author: Ling Zhang. Email: email

Journal of Information Hiding and Privacy Protection 2022, 4(2), 69-78. https://doi.org/10.32604/jihpp.2022.039284

Abstract

Deep learning is widely used in artificial intelligence fields such as computer vision, natural language recognition, and intelligent robots. With the development of deep learning, people’s expectations for this technology are increasing daily. Enterprises and individuals usually need a lot of computing power to support the practical work of deep learning technology. Many cloud service providers provide and deploy cloud computing environments. However, there are severe risks of privacy leakage when transferring data to cloud service providers and using data for model training, which makes users unable to use deep learning technology in cloud computing environments confidently. This paper mainly reviews the privacy leakage problems that exist when using deep learning, then introduces deep learning algorithms that support privacy protection, compares and looks forward to these algorithms, and summarizes this aspect’s development.

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Cite This Article

APA Style
Zhang, L., Nie, L., Yu, L. (2022). A survey of privacy preservation for deep learning applications. Journal of Information Hiding and Privacy Protection, 4(2), 69-78. https://doi.org/10.32604/jihpp.2022.039284
Vancouver Style
Zhang L, Nie L, Yu L. A survey of privacy preservation for deep learning applications. J Inf Hiding Privacy Protection . 2022;4(2):69-78 https://doi.org/10.32604/jihpp.2022.039284
IEEE Style
L. Zhang, L. Nie, and L. Yu "A Survey of Privacy Preservation for Deep Learning Applications," J. Inf. Hiding Privacy Protection , vol. 4, no. 2, pp. 69-78. 2022. https://doi.org/10.32604/jihpp.2022.039284



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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