{"total":18,"rows":[{"id":"2203.06735","title":"Private Non-Convex Federated Learning Without a Trusted Server","created":"2022-03-13","cited_by_count":26,"area":null},{"id":"2212.06074","title":"Regression with Label Differential Privacy","created":"2022-12-12","cited_by_count":15,"area":null},{"id":"2110.11208","title":"User-Level Private Learning via Correlated Sampling","created":"2021-10-21","cited_by_count":9,"area":null},{"id":"2204.03919","title":"Network Shuffling: Privacy Amplification via Random Walks","created":"2022-04-08","cited_by_count":6,"area":null},{"id":"2307.15017","title":"Samplable Anonymous Aggregation for Private Federated Data Analysis","created":"2023-07-27","cited_by_count":6,"area":null},{"id":"2312.05659","title":"Optimal Unbiased Randomizers for Regression with Label Differential Privacy","created":"2023-12-09","cited_by_count":6,"area":null},{"id":"2202.10618","title":"Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation","created":"2022-02-22","cited_by_count":5,"area":null},{"id":"2404.10201","title":"Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages","created":"2024-04-16","cited_by_count":5,"area":null},{"id":"2201.12018","title":"Transfer Learning In Differential Privacy's Hybrid-Model","created":"2022-01-28","cited_by_count":3,"area":null},{"id":"2502.14087","title":"Learning from End User Data with Shuffled Differential Privacy over Kernel Densities","created":"2025-02-19","cited_by_count":3,"area":null},{"id":"2302.11152","title":"Multi-Message Shuffled Privacy in Federated Learning","created":"2023-02-22","cited_by_count":2,"area":null},{"id":"2406.11649","title":"Making Old Things New: A Unified Algorithm for Differentially Private Clustering","created":"2024-06-17","cited_by_count":1,"area":null},{"id":"2406.18145","title":"Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling","created":"2024-06-26","cited_by_count":1,"area":null},{"id":"2503.11897","title":"PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors","created":"2025-03-14","cited_by_count":1,"area":null},{"id":"2602.00417","title":"Shuffle and Joint Differential Privacy for Generalized Linear Contextual Bandits","created":"2026-01-31","cited_by_count":1,"area":null},{"id":"2110.06559","title":"Infinitely Divisible Noise in the Low Privacy Regime","created":"2021-10-13","cited_by_count":0,"area":null},{"id":"2410.12045","title":"Differential Privacy on Trust Graphs","created":"2024-10-15","cited_by_count":0,"area":null},{"id":"2604.28032","title":"Shuffling-Aware Optimization for Private Vector Mean Estimation","created":"2026-04-30","cited_by_count":0,"area":null}],"next_cursor":null}