{"total":226,"rows":[{"id":"2101.04535","title":"Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning","created":"2021-01-11","cited_by_count":199,"area":null},{"id":"2303.00654","title":"How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy","created":"2023-03-01","cited_by_count":153,"area":null},{"id":"2108.01624","title":"Large-Scale Differentially Private BERT","created":"2021-08-03","cited_by_count":118,"area":null},{"id":"2110.03620","title":"Hyperparameter Tuning with Renyi Differential Privacy","created":"2021-10-07","cited_by_count":104,"area":null},{"id":"2309.11765","title":"Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation","created":"2023-09-21","cited_by_count":96,"area":null},{"id":"2102.12677","title":"Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning","created":"2021-02-25","cited_by_count":86,"area":null},{"id":"2106.08567","title":"Optimal Accounting of Differential Privacy via Characteristic Function","created":"2021-06-16","cited_by_count":86,"area":null},{"id":"2206.00187","title":"DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training","created":"2022-06-01","cited_by_count":74,"area":null},{"id":"2102.05855","title":"Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent","created":"2021-02-11","cited_by_count":73,"area":null},{"id":"2108.12944","title":"Selective Differential Privacy for Language Modeling","created":"2021-08-30","cited_by_count":62,"area":null},{"id":"2111.01177","title":"Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence","created":"2021-11-01","cited_by_count":54,"area":null},{"id":"2305.01639","title":"Privacy-Preserving In-Context Learning for Large Language Models","created":"2023-05-02","cited_by_count":50,"area":null},{"id":"2207.00160","title":"When Does Differentially Private Learning Not Suffer in High Dimensions?","created":"2022-07-01","cited_by_count":48,"area":null},{"id":"2210.00597","title":"Composition of Differential Privacy & Privacy Amplification by Subsampling","created":"2022-10-02","cited_by_count":45,"area":null},{"id":"2401.04343","title":"Private Fine-tuning of Large Language Models with Zeroth-order Optimization","created":"2024-01-09","cited_by_count":41,"area":null},{"id":"2109.08907","title":"Releasing Graph Neural Networks with Differential Privacy Guarantees","created":"2021-09-18","cited_by_count":32,"area":null},{"id":"2204.07667","title":"Just Fine-tune Twice: Selective Differential Privacy for Large Language Models","created":"2022-04-15","cited_by_count":32,"area":null},{"id":"2209.15328","title":"Sparse Random Networks for Communication-Efficient Federated Learning","created":"2022-09-30","cited_by_count":32,"area":null},{"id":"2306.05131","title":"Conformal Prediction for Federated Uncertainty Quantification Under Label Shift","created":"2023-06-08","cited_by_count":29,"area":null},{"id":"2407.07737","title":"Fine-Tuning Large Language Models with User-Level Differential Privacy","created":"2024-07-10","cited_by_count":29,"area":null},{"id":"2206.05091","title":"Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging","created":"2022-06-10","cited_by_count":27,"area":null},{"id":"2111.04906","title":"The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection","created":"2021-11-09","cited_by_count":25,"area":null},{"id":"2401.17319","title":"Decentralized Federated Learning: A Survey on Security and Privacy","created":"2024-01-25","cited_by_count":25,"area":null},{"id":"2103.01953","title":"Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation","created":"2021-03-02","cited_by_count":22,"area":null},{"id":"2304.01541","title":"Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation","created":"2023-04-04","cited_by_count":22,"area":null},{"id":"2102.12412","title":"Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT","created":"2021-02-24","cited_by_count":20,"area":null},{"id":"2206.02617","title":"Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent","created":"2022-06-06","cited_by_count":20,"area":null},{"id":"2301.09112","title":"Differentially Private Natural Language Models: Recent Advances and Future Directions","created":"2023-01-22","cited_by_count":20,"area":null},{"id":"2309.17310","title":"Leave-one-out Distinguishability in Machine Learning","created":"2023-09-29","cited_by_count":20,"area":null},{"id":"2107.01848","title":"Differentially Private Sliced Wasserstein Distance","created":"2021-07-05","cited_by_count":19,"area":null},{"id":"2301.11989","title":"Practical Differentially Private Hyperparameter Tuning with Subsampling","created":"2023-01-27","cited_by_count":19,"area":null},{"id":"2308.15709","title":"Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation","created":"2023-08-30","cited_by_count":19,"area":null},{"id":"2112.13178","title":"Gradient Leakage Attack Resilient Deep Learning","created":"2021-12-25","cited_by_count":18,"area":null},{"id":"2211.06387","title":"\\~Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization","created":"2022-11-11","cited_by_count":18,"area":null},{"id":"2102.01258","title":"Local Differential Privacy Is Equivalent to Contraction of $E_\\gamma$-Divergence","created":"2021-02-02","cited_by_count":17,"area":null},{"id":"2103.01294","title":"Wide Network Learning with Differential Privacy","created":"2021-03-01","cited_by_count":17,"area":null},{"id":"2301.00457","title":"ReSQueing Parallel and Private Stochastic Convex Optimization","created":"2023-01-01","cited_by_count":17,"area":null},{"id":"2307.03928","title":"Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy","created":"2023-07-08","cited_by_count":15,"area":null},{"id":"2502.08202","title":"Privacy amplification by random allocation","created":"2025-02-12","cited_by_count":15,"area":null},{"id":"2507.06969","title":"Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy","created":"2025-07-09","cited_by_count":15,"area":null},{"id":"2101.09878","title":"Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy","created":"2021-01-25","cited_by_count":14,"area":null},{"id":"2112.05120","title":"On Convergence of Federated Averaging Langevin Dynamics","created":"2021-12-09","cited_by_count":14,"area":null},{"id":"2206.04236","title":"Edgeworth Accountant: An Analytical Approach to Differential Privacy Composition","created":"2022-06-09","cited_by_count":14,"area":null},{"id":"2305.06473","title":"Securing Distributed SGD against Gradient Leakage Threats","created":"2023-05-10","cited_by_count":14,"area":null},{"id":"2402.07818","title":"Differentially Private Zeroth-Order Methods for Scalable Large Language Model Finetuning","created":"2024-02-12","cited_by_count":14,"area":null},{"id":"2205.01863","title":"Provably Confidential Language Modelling","created":"2022-05-04","cited_by_count":13,"area":null},{"id":"2208.03409","title":"DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders","created":"2022-08-05","cited_by_count":13,"area":null},{"id":"2210.09634","title":"DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling","created":"2022-10-18","cited_by_count":13,"area":null},{"id":"2305.10994","title":"Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility","created":"2023-05-18","cited_by_count":13,"area":null},{"id":"2310.19973","title":"Unified Enhancement of Privacy Bounds for Mixture Mechanisms via $f$-Differential Privacy","created":"2023-10-30","cited_by_count":13,"area":null},{"id":"2405.20769","title":"Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition","created":"2024-05-27","cited_by_count":13,"area":null},{"id":"2302.01190","title":"On the Efficacy of Differentially Private Few-shot Image Classification","created":"2023-02-02","cited_by_count":12,"area":null},{"id":"2309.12500","title":"User-Level Differential Privacy With Few Examples Per User","created":"2023-09-21","cited_by_count":12,"area":null},{"id":"2401.00583","title":"Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners","created":"2023-12-31","cited_by_count":12,"area":null},{"id":"2305.16202","title":"DP-SGD Without Clipping: The Lipschitz Neural Network Way","created":"2023-05-25","cited_by_count":11,"area":null},{"id":"2310.20682","title":"Compression with Exact Error Distribution for Federated Learning","created":"2023-10-31","cited_by_count":11,"area":null},{"id":"2403.05681","title":"DP-TabICL: In-Context Learning with Differentially Private Tabular Data","created":"2024-03-08","cited_by_count":11,"area":null},{"id":"2110.11688","title":"Differentially Private Coordinate Descent for Composite Empirical Risk Minimization","created":"2021-10-22","cited_by_count":10,"area":null},{"id":"2202.03129","title":"Over-the-Air Ensemble Inference with Model Privacy","created":"2022-02-07","cited_by_count":10,"area":null},{"id":"2202.10517","title":"Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees","created":"2022-02-21","cited_by_count":10,"area":null},{"id":"2203.09205","title":"SoK: Differential Privacy on Graph-Structured Data","created":"2022-03-17","cited_by_count":10,"area":null},{"id":"2210.06140","title":"Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies","created":"2022-10-12","cited_by_count":10,"area":null},{"id":"2210.06328","title":"Momentum Aggregation for Private Non-convex ERM","created":"2022-10-12","cited_by_count":10,"area":null},{"id":"2310.20579","title":"Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks","created":"2023-10-31","cited_by_count":10,"area":null},{"id":"2407.06496","title":"It's Our Loss: No Privacy Amplification for Hidden State DP-SGD With Non-Convex Loss","created":"2024-07-09","cited_by_count":10,"area":null},{"id":"2206.01836","title":"Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization","created":"2022-06-03","cited_by_count":9,"area":null},{"id":"2302.08661","title":"Subsampling Suffices for Adaptive Data Analysis","created":"2023-02-17","cited_by_count":9,"area":null},{"id":"2309.07809","title":"Communication Efficient Private Federated Learning Using Dithering","created":"2023-09-14","cited_by_count":9,"area":null},{"id":"2311.01295","title":"DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning","created":"2023-11-02","cited_by_count":9,"area":null},{"id":"2401.16251","title":"Cross-silo Federated Learning with Record-level Personalized Differential Privacy","created":"2024-01-29","cited_by_count":9,"area":null},{"id":"2405.06368","title":"DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation","created":"2024-05-10","cited_by_count":9,"area":null},{"id":"2112.00845","title":"Improving Differentially Private SGD via Randomly Sparsified Gradients","created":"2021-12-01","cited_by_count":8,"area":null},{"id":"2206.00511","title":"Differentially Private Shapley Values for Data Evaluation","created":"2022-06-01","cited_by_count":8,"area":null},{"id":"2405.08920","title":"Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning","created":"2024-05-14","cited_by_count":8,"area":null},{"id":"2408.10456","title":"Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement","created":"2024-08-19","cited_by_count":8,"area":null},{"id":"2409.09558","title":"A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem","created":"2024-09-14","cited_by_count":8,"area":null},{"id":"2301.00955","title":"Differentially Private Federated Clustering over Non-IID Data","created":"2023-01-03","cited_by_count":7,"area":null},{"id":"2307.04733","title":"A unifying framework for differentially private quantum algorithms","created":"2023-07-10","cited_by_count":7,"area":null},{"id":"2307.15424","title":"Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis","created":"2023-07-28","cited_by_count":7,"area":null},{"id":"2311.14465","title":"DP-NMT: Scalable Differentially-Private Machine Translation","created":"2023-11-24","cited_by_count":7,"area":null},{"id":"2503.03043","title":"Leveraging Randomness in Model and Data Partitioning for Privacy Amplification","created":"2025-03-04","cited_by_count":7,"area":null},{"id":"2107.08461","title":"Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability","created":"2021-07-18","cited_by_count":6,"area":null},{"id":"2203.03604","title":"Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms","created":"2022-03-07","cited_by_count":6,"area":null},{"id":"2204.03919","title":"Network Shuffling: Privacy Amplification via Random Walks","created":"2022-04-08","cited_by_count":6,"area":null},{"id":"2308.07553","title":"Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks","created":"2023-08-15","cited_by_count":6,"area":null},{"id":"2311.10101","title":"Gaussian Differential Privacy on Riemannian Manifolds","created":"2023-11-09","cited_by_count":6,"area":null},{"id":"2406.04827","title":"Auditing Differential Privacy Guarantees Using Density Estimation","created":"2024-06-07","cited_by_count":6,"area":null},{"id":"2411.10614","title":"To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling","created":"2024-11-15","cited_by_count":6,"area":null},{"id":"2503.10945","title":"Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning","created":"2025-03-13","cited_by_count":6,"area":null},{"id":"2202.08969","title":"Private Quantiles Estimation in the Presence of Atoms","created":"2022-02-15","cited_by_count":5,"area":null},{"id":"2208.09595","title":"The Saddle-Point Accountant for Differential Privacy","created":"2022-08-20","cited_by_count":5,"area":null},{"id":"2212.13992","title":"Social-Aware Clustered Federated Learning with Customized Privacy Preservation","created":"2022-12-25","cited_by_count":5,"area":null},{"id":"2301.00738","title":"Training Differentially Private Graph Neural Networks with Random Walk Sampling","created":"2023-01-02","cited_by_count":5,"area":null},{"id":"2302.04972","title":"Differentially Private Optimization for Smooth Nonconvex ERM","created":"2023-02-09","cited_by_count":5,"area":null},{"id":"2302.12559","title":"From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning","created":"2023-02-24","cited_by_count":5,"area":null},{"id":"2304.07460","title":"Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy","created":"2023-04-15","cited_by_count":5,"area":null},{"id":"2305.00873","title":"Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy","created":"2023-05-01","cited_by_count":5,"area":null},{"id":"2402.13087","title":"Revisiting Differentially Private Hyper-parameter Tuning","created":"2024-02-20","cited_by_count":5,"area":null},{"id":"2404.10029","title":"Federated Learning on Riemannian Manifolds with Differential Privacy","created":"2024-04-15","cited_by_count":5,"area":null},{"id":"2405.18194","title":"Delving into Differentially Private Transformer","created":"2024-05-28","cited_by_count":5,"area":null}],"next_cursor":"100"}