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Balancing utility and scalability in metric differential privacy.

, , , , and . UAI, volume 180 of Proceedings of Machine Learning Research, page 885-894. PMLR, (2022)

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Research Challenges in Designing Differentially Private Text Generation Mechanisms., , , and . CoRR, (2020)On Codomain Separability and Label Inference from (Noisy) Loss Functions., , , , and . CoRR, (2021)On a Utilitarian Approach to Privacy Preserving Text Generation., , , and . CoRR, (2021)Label Inference Attacks from Log-loss Scores., , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 120-129. PMLR, (2021)Research Challenges in Designing Differentially Private Text Generation Mechanisms., , , and . FLAIRS, (2021)Differentially Private Adversarial Robustness Through Randomized Perturbations., , , , and . CoRR, (2020)Density-Aware Differentially Private Textual Perturbations Using Truncated Gumbel Noise., , , , and . FLAIRS, (2021)A Differentially Private Text Perturbation Method Using a Regularized Mahalanobis Metric., , , and . CoRR, (2020)Balancing utility and scalability in metric differential privacy., , , , and . UAI, volume 180 of Proceedings of Machine Learning Research, page 885-894. PMLR, (2022)On Primes, Log-Loss Scores and (No) Privacy., , , and . CoRR, (2020)