{"total":15,"rows":[{"id":"2206.07902","title":"On Privacy and Personalization in Cross-Silo Federated Learning","created":"2022-06-16","cited_by_count":36,"area":null},{"id":"2202.05567","title":"Shuffle Private Linear Contextual Bandits","created":"2022-02-11","cited_by_count":27,"area":null},{"id":"2309.02202","title":"On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence","created":"2023-09-05","cited_by_count":6,"area":null},{"id":"2404.01141","title":"SoK: A Review of Differentially Private Linear Models For High-Dimensional Data","created":"2024-04-01","cited_by_count":5,"area":null},{"id":"2206.09569","title":"Shuffle Gaussian Mechanism for Differential Privacy","created":"2022-06-20","cited_by_count":4,"area":null},{"id":"2301.12535","title":"Concurrent Shuffle Differential Privacy Under Continual Observation","created":"2023-01-29","cited_by_count":2,"area":null},{"id":"2412.07454","title":"Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning","created":"2024-12-10","cited_by_count":2,"area":null},{"id":"2505.15721","title":"Privacy-Preserving Conformal Prediction Under Local Differential Privacy","created":"2025-05-21","cited_by_count":2,"area":null},{"id":"2307.07744","title":"On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms","created":"2023-07-15","cited_by_count":1,"area":null},{"id":"2308.09902","title":"DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning","created":"2023-08-19","cited_by_count":1,"area":null},{"id":"2507.01487","title":"How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations","created":"2025-07-02","cited_by_count":1,"area":null},{"id":"2108.08987","title":"Uniformity Testing in the Shuffle Model: Simpler, Better, Faster","created":"2021-08-20","cited_by_count":0,"area":null},{"id":"2304.11741","title":"Robust and differentially private stochastic linear bandits","created":"2023-04-23","cited_by_count":0,"area":null},{"id":"2606.02563","title":"IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2608.12100","title":"Confidence Calibration of Deep Learning Systems","created":"2026-08-12","cited_by_count":0,"area":null}],"next_cursor":null}