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Reconstruction and Membership Inference Attacks against Generative Models.

, , и . CoRR, (2019)

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On the privacy-utility trade-off in differentially private hierarchical text classification., , , , и . CoRR, (2021)Quantifying identifiability to choose and audit epsilon in differentially private deep learning., , , , и . Proc. VLDB Endow., 14 (13): 3335-3347 (2021)Improved usability of differential privacy in machine learning: techniques for quantifying the privacy-accuracy trade-off.. University of Stuttgart, Germany, (2022)Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models., , и . Proc. Priv. Enhancing Technol., 2019 (4): 232-249 (2019)Quantifying identifiability to choose and audit ε in differentially private deep learning., , , , и . CoRR, (2021)Reconstruction and Membership Inference Attacks against Generative Models., , и . CoRR, (2019)Assessing differentially private deep learning with Membership Inference., , , и . CoRR, (2019)Comparing Local and Central Differential Privacy Using Membership Inference Attacks., , , , и . DBSec, том 12840 из Lecture Notes in Computer Science, стр. 22-42. Springer, (2021)Assessing Differentially Private Variational Autoencoders Under Membership Inference., , и . DBSec, том 13383 из Lecture Notes in Computer Science, стр. 3-14. Springer, (2022)