Differential Privacy

Private High-Dimensional Hypothesis Testing COLT 2022 Private Robust Estimation by Stabilizing Convex Relaxations COLT 2022 The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection AAAI 2022 Deep Learning with Label Differential Privacy NeurIPS 2021 Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning ICLR 2021 Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization NeurIPS 2021 H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning IJCAI 2021 (Nearly) Dimension Independent Private ERM with AdaGrad Rates\{via Publicly Estimated Subspaces COLT 2021 Scalable Differential Privacy With Sparse Network Finetuning CVPR 2021 Tempered Sigmoid Activations for Deep Learning with Differential Privacy AAAI 2021 The Sparse Vector Technique, Revisited COLT 2021 Efficient, Noise-Tolerant, and Private Learning via Boosting COLT 2020 Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms NeurIPS 2020 PAC learning with stable and private predictions COLT 2020 Differentially Private Markov Chain Monte Carlo NeurIPS 2019 Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy NeurIPS 2019 Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning? COLT 2019 cpSGD: Communication-efficient and differentially-private distributed SGD NeurIPS 2018 Distributed Fine-tuning of Language Models on Private Data ICLR 2018 Model-Agnostic Private Learning NeurIPS 2018 Privacy Odometers and Filters: Pay-as-you-Go Composition NeurIPS 2016 Differentially Private Learning of Structured Discrete Distributions NeurIPS 2015 Generalization in Adaptive Data Analysis and Holdout Reuse NeurIPS 2015 Secure Multi-party Differential Privacy NeurIPS 2015 Extremal Mechanisms for Local Differential Privacy NeurIPS 2014