differential privacy

Near-Optimal Correlation Clustering with Privacy NeurIPS 2022 Near-Optimal Private and Scalable k-Clustering NeurIPS 2022 Near-optimal Thompson sampling-based algorithms for differentially private stochastic bandits UAI 2022 New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma NeurIPS 2022 One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks EMNLP 2022 On Privacy and Personalization in Cross-Silo Federated Learning NeurIPS 2022 Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees COLT 2022 Open Problem: Do you pay for Privacy in Online learning? COLT 2022 Optimal Accounting of Differential Privacy via Characteristic Function AISTATS 2022 Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits AISTATS 2022 Order-Invariant Cardinality Estimators Are Differentially Private NeurIPS 2022 Parametric Bootstrap for Differentially Private Confidence Intervals AISTATS 2022 PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning ICLR 2022 Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning ICML 2022 Post-processing of Differentially Private Data: A Fairness Perspective IJCAI 2022 Privacy Amplification by Decentralization AISTATS 2022 Privacy Amplification by Subsampling in Time Domain AISTATS 2022 Privacy Amplification via Shuffling for Linear Contextual Bandits ALT 2022 Privacy-aware compression for federated data analysis UAI 2022 Privacy for Free: How does Dataset Condensation Help Privacy? ICML 2022 Privacy Implications of Shuffling ICLR 2022 Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation NeurIPS 2022 Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss NeurIPS 2022 Privacy-Preserving Models for Legal Natural Language Processing EMNLP 2022 Private Adaptive Optimization with Side information ICML 2022