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Generative Adversarial Networks (GAN)-synthesized table publishing lets people privately learn insights without access to the private table.
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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In Proceedings of the 2019 Network and Distributed Systems Security Symposium
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. 2019 · 2019
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GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (CCS 2020) . ACM, New York, NY, USA
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Label-Leaks: Membership Inference Attack with Label
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On the Privacy Properties of GAN-generated Samples. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 130) , Arindam Banerjee and Kenji Fukumizu (Eds.). PMLR, 1522–1530
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