{"total":18,"rows":[{"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":"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":"2102.08598","title":"Leveraging Public Data for Practical Private Query Release","created":"2021-02-17","cited_by_count":39,"area":null},{"id":"2206.07902","title":"On Privacy and Personalization in Cross-Silo Federated Learning","created":"2022-06-16","cited_by_count":36,"area":null},{"id":"2305.01177","title":"Unbounded Differentially Private Quantile and Maximum Estimation","created":"2023-05-02","cited_by_count":6,"area":null},{"id":"2402.06137","title":"On the Privacy of Selection Mechanisms with Gaussian Noise","created":"2024-02-09","cited_by_count":3,"area":null},{"id":"2201.13376","title":"Differentially Private Top-k Selection via Canonical Lipschitz Mechanism","created":"2022-01-31","cited_by_count":2,"area":null},{"id":"2308.03735","title":"Randomized algorithms for precise measurement of differentially-private, personalized recommendations","created":"2023-08-07","cited_by_count":1,"area":null},{"id":"2407.20068","title":"Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noise","created":"2024-07-29","cited_by_count":1,"area":null},{"id":"2408.06997","title":"Faster Private Minimum Spanning Trees","created":"2024-08-13","cited_by_count":1,"area":null},{"id":"2502.10997","title":"Improved Regret in Stochastic Decision-Theoretic Online Learning under Differential Privacy","created":"2025-02-16","cited_by_count":1,"area":null},{"id":"2306.06721","title":"Differentially Private Conditional Independence Testing","created":"2023-06-11","cited_by_count":0,"area":null},{"id":"2412.10130","title":"Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation","created":"2024-12-13","cited_by_count":0,"area":null},{"id":"2501.05309","title":"Private Selection with Heterogeneous Sensitivities","created":"2025-01-09","cited_by_count":0,"area":null},{"id":"2502.05307","title":"Training Set Reconstruction from Differentially Private Forests: How Effective is DP?","created":"2025-02-07","cited_by_count":0,"area":null},{"id":"2504.08086","title":"Differentially Private Selection using Smooth Sensitivity","created":"2025-04-10","cited_by_count":0,"area":null},{"id":"2605.22756","title":"Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2607.13928","title":"Plausible Deniability Guarantees for Whistleblowers","created":"2026-07-15","cited_by_count":0,"area":null}],"next_cursor":null}