differential privacy

Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT AISTATS 2021 Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms UAI 2021 Towards Fair and Transparent Algorithmic Systems IJCAI 2021 Understanding Unintended Memorization in Language Models Under Federated Learning NAACL 2021 User-Level Differentially Private Learning via Correlated Sampling NeurIPS 2021 When differential privacy meets NLP: The devil is in the detail EMNLP 2021 A Computational Separation between Private Learning and Online Learning NeurIPS 2020 A Differentially Private Text Perturbation Method Using Regularized Mahalanobis Metric EMNLP 2020 Adversarially Robust Streaming Algorithms via Differential Privacy NeurIPS 2020 A Knowledge Transfer Framework for Differentially Private Sparse Learning AAAI 2020 Alleviating Privacy Attacks via Causal Learning ICML 2020 An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm ICML 2020 Auditing Differentially Private Machine Learning: How Private is Private SGD? NeurIPS 2020 Bayesian Differential Privacy for Machine Learning ICML 2020 Bayesian Pseudocoresets NeurIPS 2020 Breaking the Communication-Privacy-Accuracy Trilemma NeurIPS 2020 CoinPress: Practical Private Mean and Covariance Estimation NeurIPS 2020 Computing Tight Differential Privacy Guarantees Using FFT AISTATS 2020 Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models ICML 2020 Differentially Private and Fair Classification via Calibrated Functional Mechanism AAAI 2020 Differentially Private Clustering: Tight Approximation Ratios NeurIPS 2020 Differentially-Private Federated Linear Bandits NeurIPS 2020 Differentially Private Language Models Benefit from Public Pre-training EMNLP 2020 Differentially Private Learning with Small Public Data AAAI 2020 Differentially Private Meta-Learning ICLR 2020