{"total":24,"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":"2305.15560","title":"Differentially Private Synthetic Data via Foundation Model APIs 1: Images","created":"2023-05-24","cited_by_count":65,"area":null},{"id":"2104.09734","title":"Locally Private k-Means in One Round","created":"2021-04-20","cited_by_count":19,"area":null},{"id":"2112.14445","title":"Differentially-Private Clustering of Easy Instances","created":"2021-12-29","cited_by_count":17,"area":null},{"id":"2208.01700","title":"Differentially Private Vertical Federated Clustering","created":"2022-08-02","cited_by_count":10,"area":null},{"id":"2504.06923","title":"The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data","created":"2025-04-09","cited_by_count":5,"area":null},{"id":"2506.04566","title":"Clustering and Median Aggregation Improve Differentially Private Inference","created":"2025-06-05","cited_by_count":5,"area":null},{"id":"2402.08012","title":"Online Differentially Private Synthetic Data Generation","created":"2024-02-12","cited_by_count":4,"area":null},{"id":"2112.13751","title":"Differentially-Private Sublinear-Time Clustering","created":"2021-12-27","cited_by_count":2,"area":null},{"id":"2301.02896","title":"k-Means SubClustering: A Differentially Private Algorithm with Improved Clustering Quality","created":"2023-01-07","cited_by_count":2,"area":null},{"id":"2405.02437","title":"FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy","created":"2024-05-03","cited_by_count":2,"area":null},{"id":"2104.07938","title":"Achieving differential privacy for $k$-nearest neighbors based outlier detection by data partitioning","created":"2021-04-16","cited_by_count":1,"area":null},{"id":"2106.12959","title":"Differentially Private Algorithms for Clustering with Stability Assumptions","created":"2021-06-11","cited_by_count":1,"area":null},{"id":"2307.02969","title":"DPM: Clustering Sensitive Data through Separation","created":"2023-07-06","cited_by_count":1,"area":null},{"id":"2208.12553","title":"I still know it's you! On Challenges in Anonymizing Source Code","created":"2022-08-26","cited_by_count":0,"area":null},{"id":"2304.13886","title":"Improving the Utility of Differentially Private Clustering through Dynamical Processing","created":"2023-04-27","cited_by_count":0,"area":null},{"id":"2307.10187","title":"Personalized Privacy Amplification via Importance Sampling","created":"2023-07-05","cited_by_count":0,"area":null},{"id":"2411.19045","title":"Aggregating Data for Optimal and Private Learning","created":"2024-11-28","cited_by_count":0,"area":null},{"id":"2412.02578","title":"Private Linear Regression with Differential Privacy and PAC Privacy","created":"2024-12-03","cited_by_count":0,"area":null},{"id":"2504.07578","title":"Privacy-Preserving Vertical K-Means Clustering","created":"2025-04-10","cited_by_count":0,"area":null},{"id":"2506.16661","title":"Private Training & Data Generation by Clustering Embeddings","created":"2025-06-20","cited_by_count":0,"area":null},{"id":"2602.01607","title":"Minimax optimal differentially private synthetic data for smooth queries","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2606.00342","title":"PE-means: Improved Differentially Private $k$-means Clustering through Private Evolution","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2609.33363","title":"Geometry-Adaptive Mechanisms for Private Synthetic Data","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}