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Conference Paper

GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators

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Orekondy,  Tribhuvanesh
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Fritz,  Mario
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Chen, D., Orekondy, T., & Fritz, M. (2020). GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33 (pp. 12673-12684). Curran Associates, Inc.


Cite as: http://hdl.handle.net/21.11116/0000-0008-1866-B
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