Generative Adversarial Nets Enhanced Continual Data Release Using Differential Privacy-Reference-Cited by-同舟云学术

Generative Adversarial Nets Enhanced Continual Data Release Using Differential Privacy

Published:2020 Issue: Volume: Page:418-426
ISSN:0302-9743
Container-title:Algorithms and Architectures for Parallel Processing
language:
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Author:

Ho Stella,Qu Youyang,Gao Longxing,Li Jianxin,Xiang Yong

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-38961-1_37

Reference21 articles.

1. Audebert, N., Le Saux, B., Lefevre, S.: Generative adversarial networks for realistic synthesis of hyperspectral samples. In: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 4359–4362 (2018)

2. Cao, Y., Yoshikawa, M., Xiao, Y., Xiong, L.: Quantifying differential privacy in continuous data release under temporal correlations. IEEE Trans. Knowl. Data Eng. 9, 1281–1295 (2018)

3. Chan, T.H.H., Shi, E., Song, D.: Private and continual release of statistics. ACM Trans. Inf. Syst. Secur. (TISSEC) 14(3), 26 (2011)

4. Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml

5. Lecture Notes in Computer Science;C Dwork,2006

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