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