{"total":34,"rows":[{"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":"2110.11876","title":"Tight and Robust Private Mean Estimation with Few Users","created":"2021-10-22","cited_by_count":26,"area":null},{"id":"2104.09734","title":"Locally Private k-Means in One Round","created":"2021-04-20","cited_by_count":19,"area":null},{"id":"2302.10359","title":"Replicable Clustering","created":"2023-02-20","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":"2206.08646","title":"Scalable Differentially Private Clustering via Hierarchically Separated Trees","created":"2022-06-17","cited_by_count":10,"area":null},{"id":"2208.01700","title":"Differentially Private Vertical Federated Clustering","created":"2022-08-02","cited_by_count":10,"area":null},{"id":"2410.12085","title":"Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning","created":"2024-10-15","cited_by_count":10,"area":null},{"id":"2307.07604","title":"Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes","created":"2023-07-14","cited_by_count":8,"area":null},{"id":"2203.01440","title":"Near-Optimal Correlation Clustering with Privacy","created":"2022-03-02","cited_by_count":6,"area":null},{"id":"2302.00037","title":"Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees","created":"2023-01-31","cited_by_count":5,"area":null},{"id":"2206.12895","title":"$k$-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy","created":"2022-06-26","cited_by_count":4,"area":null},{"id":"2506.04681","title":"Urania: Differentially Private Insights into AI Use","created":"2025-06-05","cited_by_count":4,"area":null},{"id":"2111.12786","title":"Differentially Private Nonparametric Regression Under a Growth Condition","created":"2021-11-24","cited_by_count":3,"area":null},{"id":"2510.00076","title":"Private Learning of Littlestone Classes, Revisited","created":"2025-09-30","cited_by_count":3,"area":null},{"id":"2112.13751","title":"Differentially-Private Sublinear-Time Clustering","created":"2021-12-27","cited_by_count":2,"area":null},{"id":"2207.01771","title":"A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy","created":"2022-07-05","cited_by_count":2,"area":null},{"id":"2307.03430","title":"Differential Privacy for Clustering Under Continual Observation","created":"2023-07-07","cited_by_count":2,"area":null},{"id":"2403.14332","title":"A Differentially Private Clustering Algorithm for Well-Clustered Graphs","created":"2024-03-21","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":"2407.04783","title":"Agnostic Private Density Estimation for GMMs via List Global Stability","created":"2024-07-05","cited_by_count":2,"area":null},{"id":"2412.10512","title":"Differentially Private Multi-Sampling from Distributions","created":"2024-12-13","cited_by_count":2,"area":null},{"id":"2510.03021","title":"Differentially Private Wasserstein Barycenters","created":"2025-10-03","cited_by_count":2,"area":null},{"id":"2207.10240","title":"Differentially Private Partial Set Cover with Applications to Facility Location","created":"2022-07-21","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":"2307.07449","title":"Differentially Private Clustering in Data Streams","created":"2023-07-14","cited_by_count":1,"area":null},{"id":"2402.06629","title":"Towards the mathematical foundation of the minimum enclosing ball and related problems","created":"2024-01-09","cited_by_count":1,"area":null},{"id":"2406.04610","title":"Contrastive Explainable Clustering with Differential Privacy","created":"2024-06-07","cited_by_count":1,"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":"2307.10187","title":"Personalized Privacy Amplification via Importance Sampling","created":"2023-07-05","cited_by_count":0,"area":null},{"id":"2509.06214","title":"Metric Embedding Initialization-Based Differentially Private and Explainable Graph Clustering","created":"2025-09-07","cited_by_count":0,"area":null},{"id":"2510.23438","title":"Coresets for Clustering Under Stochastic Noise","created":"2025-10-27","cited_by_count":0,"area":null},{"id":"2603.26963","title":"On the Optimal Number of Grids for Differentially Private Non-Interactive $K$-Means Clustering","created":"2026-03-27","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}],"next_cursor":null}