differential privacy

Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification IJCAI 2022 What You See is What You Get: Principled Deep Learning via Distributional Generalization NeurIPS 2022 When Does Differentially Private Learning Not Suffer in High Dimensions? NeurIPS 2022 When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits NeurIPS 2022 Accuracy, Interpretability, and Differential Privacy via Explainable Boosting ICML 2021 A Central Limit Theorem for Differentially Private Query Answering NeurIPS 2021 Adapting to function difficulty and growth conditions in private optimization NeurIPS 2021 Adaptive Machine Unlearning NeurIPS 2021 Adaptive Privacy Preserving Deep Learning Algorithms for Medical Data WACV 2021 ADePT: Auto-encoder based Differentially Private Text Transformation EACL 2021 A Framework for Private Matrix Analysis in Sliding Window Model ICML 2021 Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels NeurIPS 2021 An Investigation towards Differentially Private Sequence Tagging in a Federated Framework NAACL 2021 An Uncertainty Principle is a Price of Privacy-Preserving Microdata NeurIPS 2021 Bias and Variance of Post-processing in Differential Privacy AAAI 2021 Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics ALT 2021 Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification ICLR 2021 CAPE: Context-Aware Private Embeddings for Private Language Learning EMNLP 2021 Certified Robustness to Word Substitution Attack with Differential Privacy NAACL 2021 Configurable Privacy-Preserving Automatic Speech Recognition Interspeech 2021 Constrained differentially private federated learning for low-bandwidth devices UAI 2021 Covariance-Aware Private Mean Estimation Without Private Covariance Estimation NeurIPS 2021 Decision Making with Differential Privacy under a Fairness Lens IJCAI 2021 Deep Learning with Label Differential Privacy NeurIPS 2021 Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message ICML 2021