differential privacy

All Rivers Run to the Sea: Private Learning with Asymmetric Flows CVPR 2024 Analysis of Differentially Private Synthetic Data: A Measurement Error Approach AAAI 2024 Analysis of Privacy Leakage in Federated Large Language Models AISTATS 2024 A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions ALT 2024 A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy AAAI 2024 Attack-Aware Noise Calibration for Differential Privacy NeurIPS 2024 Auditing Privacy Mechanisms via Label Inference Attacks NeurIPS 2024 Auditing Private Prediction ICML 2024 A Unified Characterization of Private Learnability via Graph Theory COLT 2024 Banded Square Root Matrix Factorization for Differentially Private Model Training NeurIPS 2024 Beyond the Calibration Point: Mechanism Comparison in Differential Privacy ICML 2024 Can Public Large Language Models Help Private Cross-device Federated Learning? NAACL 2024 CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources ICML 2024 Certified Machine Unlearning via Noisy Stochastic Gradient Descent NeurIPS 2024 Certified private data release for sparse Lipschitz functions AISTATS 2024 Characterizing Stereotypical Bias from Privacy-preserving Pre-Training ACL 2024 Compression with Exact Error Distribution for Federated Learning AISTATS 2024 Computing epidemic metrics with edge differential privacy AISTATS 2024 Concealing Sensitive Samples against Gradient Leakage in Federated Learning AAAI 2024 Confidential-DPproof: Confidential Proof of Differentially Private Training ICLR 2024 Continual Counting with Gradual Privacy Expiration NeurIPS 2024 Correlated Noise Provably Beats Independent Noise for Differentially Private Learning ICLR 2024 Credit Attribution and Stable Compression NeurIPS 2024 Delving into Differentially Private Transformer ICML 2024 Differentially Private Conditional Independence Testing AISTATS 2024