There are challenges and issues when machine learning algorithm needs to access highly sensitive data for the training process. In order to address these issues, several privacy-preserving deep learning techniques, including Secure Multi-Party Computation and Homomorphic Encryption in Neural Network have been developed. There are also several methods to modify the Neural Network, so that it can be used in privacy-preserving environment. However, there is trade-off between privacy and performance among various techniques. In this paper, we discuss state-of-the-art of Privacy-Preserving Deep Learning, evaluate all methods, compare pros and cons of each approach, and address challenges and issues in the field of privacy-preserving by deep learning.
CITATION STYLE
Tanuwidjaja, H. C., Choi, R., & Kim, K. (2019). A Survey on Deep Learning Techniques for Privacy-Preserving. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11806 LNCS, pp. 29–46). Springer Verlag. https://doi.org/10.1007/978-3-030-30619-9_4
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