Abstract
Artificial Intelligence has been widely applied today, and the subsequent privacy leakage problems have also been paid attention to. Attacks such as model inference attacks on deep neural networks can easily extract user information from neural networks. Therefore, it is necessary to protect privacy in deep learning. Differential privacy, as a popular topic in privacy-preserving in recent years, which provides rigorous privacy guarantee, can also be used to preserve privacy in deep learning. Although many articles have proposed different methods to combine differential privacy and deep learning, there are no comprehensive papers to analyze and compare the differences and connections between these technologies. For this purpose, this paper is proposed to compare different differential private methods in deep learning. We comparatively analyze and classify several deep learning models under differential privacy. Meanwhile, we also pay attention to the application of differential privacy in Generative Adversarial Networks (GANs), comparing and analyzing these models. Finally, we summarize the application of differential privacy in deep neural networks.
Highlights
In recent years, deep learning based on neural networks has been widely developed and successfully applied to many fields, such as image classification [1], natural language processing [2], face recognition [3, 4], interpretable mechanism learning [5], and recommendation systems [6, 7]
We found that differential privacy can be applied to Generative Adversarial Networks (GAN)
Literature [52] compared pCDBN with dPAH, a human behavior recognition model using deep private autoencoder (dPA), in the experimental part. e results can be seen in Figure 2. e result shows that the accuracy of pCDBN is higher than that of dPAH
Summary
Deep learning based on neural networks has been widely developed and successfully applied to many fields, such as image classification [1], natural language processing [2], face recognition [3, 4], interpretable mechanism learning [5], and recommendation systems [6, 7]. Ere are many methods to protect user information in privacy-preserving fields, such as k-anonymity [11], homomorphic encryption [12], L-diversity [13], and secure multiparty computing [14]. Most of these methods desensitize the data or encrypt it into ciphertext [15], but they are not effective for some particular attacks. Is paper first introduces differential privacy and deep neural networks, as well as the types of attacks that neural networks may suffer from
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