Deriving an Optimal Noise Adding Mechanism for Privacy-Preserving Machine Learning-Reference-Cited by-同舟云学术

Deriving an Optimal Noise Adding Mechanism for Privacy-Preserving Machine Learning

Published:2019 Issue: Volume: Page:108-118
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
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Author:

Kumar Mohit,Rossbory Michael,Moser Bernhard A.,Freudenthaler Bernhard

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-27684-3_15

Reference14 articles.

1. Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS 2016, pp. 308–318. ACM, New York (2016)

2. Balle, B., Wang, Y.: Improving the Gaussian mechanism for differential privacy: analytical calibration and optimal denoising. CoRR abs/1805.06530 (2018)

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

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

5. Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)

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