differential privacy

Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping ICLR 2023 Fast and Differentially Private Fair Clustering IJCAI 2023 Faster approximate subgraph counts with privacy NeurIPS 2023 Faster Differentially Private Convex Optimization via Second-Order Methods NeurIPS 2023 Faster Rates of Convergence to Stationary Points in Differentially Private Optimization ICML 2023 Fast Optimal Locally Private Mean Estimation via Random Projections NeurIPS 2023 Fast Private Kernel Density Estimation via Locality Sensitive Quantization ICML 2023 Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions COLT 2023 FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning CVPR 2023 Federated Learning of Gboard Language Models with Differential Privacy ACL 2023 Federated Linear Contextual Bandits with User-level Differential Privacy ICML 2023 FedSampling: A Better Sampling Strategy for Federated Learning IJCAI 2023 FLIP: A Utility Preserving Privacy Mechanism for Time Series JMLR 2023 Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models NeurIPS 2023 Forget Unlearning: Towards True Data-Deletion in Machine Learning ICML 2023 From Adaptive Query Release to Machine Unlearning ICML 2023 From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning ICML 2023 From Robustness to Privacy and Back ICML 2023 Fully-Adaptive Composition in Differential Privacy ICML 2023 Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels JMLR 2023 Generalization in the Face of Adaptivity: A Bayesian Perspective NeurIPS 2023 Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy AISTATS 2023 Generating Private Synthetic Data with Genetic Algorithms ICML 2023 GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces NeurIPS 2023 Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy NeurIPS 2023