Titlebook: Differential Privacy and Applications; Tianqing Zhu,Gang Li,Philip S. Yu Book 2017 Springer International Publishing AG 2017 data analysis

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creatine-kinase

https://doi.org/10.1007/978-3-031-56188-7 bound or sample complexity. But private learning frameworks can only deal with limited learning algorithms, while nearly all types of analysis algorithms can be implemented in a Laplace/exponential framework.

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得意人

Lisa Wiebesiek,Relebohile Moletsaner of queries is limited, as a large volume of noise will be introduced when the number of queries increases. A method called graph update method is then presented in this chapter to solve this serious problem. The key idea of the method is to transfer the query release problem into an iteration proc

PHAG

Alexandra Budke,Kimberley Hindmarshms and utilize differential privacy to prevent the leaking of private information when releasing the dataset. A private tagging release algorithm is presented in this chapter to provide comprehensive privacy-preserving capability for individuals and maximizing the utility of the released dataset. Th

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considerable

Differentially Private Deep Learning,uted Private SGD. Each of them is focusing on a particular deep learning algorithm and is dealing with those two challenges in different ways. Finally, this chapter shows several popular datasets that can be used in differentially private deep learning.