% Key papers for Differentially private clustering (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2018_privacy_99796,
  title = {{Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11244},
}

@inproceedings{icml2017_differentially_36153,
  title = {{Differentially Private Clustering in High-Dimensional Euclidean Spaces}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/756},
}

@inproceedings{neurips2018_differentially_57041,
  title = {{Differentially Private k-Means with Constant Multiplicative Error}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11530},
}

@inproceedings{neurips2019_on_58705,
  title = {{On Differentially Private Graph Sparsification and Applications}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14277},
}

@inproceedings{aaai2021_differentially_428,
  title = {{Differentially Private Clustering via Maximum Coverage}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/11555-differentially-private-clustering-via-maximum-coverage/},
}

@inproceedings{icml2020_differentially_38105,
  title = {{Differentially Private Set Union}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6541},
}

@inproceedings{neurips2020_differentially_59700,
  title = {{Differentially Private Clustering: Tight Approximation Ratios}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18339},
}

@inproceedings{aaai2021_differentially_434,
  title = {{Differentially Private k-Means via Exponential Mechanism and Max Cover}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09101-differentially-private-k-means-via-exponential-mechanism-and-max-cover/},
}

@inproceedings{aaai2022_private_2578,
  title = {{Private Rank Aggregation in Central and Local Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/05984-private-rank-aggregation-in-central-and-local-models/},
}

@inproceedings{icml2022_differentially_40449,
  title = {{Differentially Private Community Detection for Stochastic Block Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16779},
}

@inproceedings{neurips2019_differentially_58131,
  title = {{Differentially Private Anonymized Histograms}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13872},
}

@inproceedings{aaai2022_locally_2376,
  title = {{Locally Private k-Means Clustering with Constant Multiplicative Approximation and Near-Optimal Additive Error}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/06167-locally-private-k-means-clustering-with-constant-multiplicative-approximation-and-near-optimal-additive-error/},
}

@inproceedings{icml2021_differentially_39244,
  title = {{Differentially-Private Clustering of Easy Instances}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8567},
}

@inproceedings{icml2022_friendlycore_40586,
  title = {{FriendlyCore: Practical Differentially Private Aggregation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/16323},
}

@inproceedings{neurips2020_towards_61002,
  title = {{Towards practical differentially private causal graph discovery}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18828},
}

@inproceedings{icml2021_differentially_39239,
  title = {{Differentially Private Correlation Clustering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10369},
}

@inproceedings{icml2023_sketch_41753,
  title = {{Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23976},
}

@inproceedings{neurips2023_k_67142,
  title = {{k-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72566},
}

@inproceedings{neurips2023_k_68322,
  title = {{\$k\$-Means Clustering with Distance-Based Privacy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71483},
}

@inproceedings{neurips2023_differentially_68623,
  title = {{Differentially Private Approximate Near Neighbor Counting in High Dimensions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71215},
}

@inproceedings{iclr2024_a_34987,
  title = {{A Differentially Private Clustering Algorithm for Well-Clustered Graphs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18087},
}

@inproceedings{icml2024_making_43398,
  title = {{Making Old Things New: A Unified Algorithm for Differentially Private Clustering}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35054},
}

@inproceedings{icml2024_differentially_44206,
  title = {{Differentially private exact recovery for stochastic block models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34246},
}

@inproceedings{icml2025_breaking_46230,
  title = {{Breaking the \$n^1.5\$ Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46330},
}

@inproceedings{icml2025_differentially_46607,
  title = {{Differentially Private Federated \$k\$-Means Clustering with Server-Side Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45962},
}

@inproceedings{icml2025_scalable_47076,
  title = {{Scalable Private Partition Selection via Adaptive Weighting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45510},
}

@inproceedings{icml2026_differentially_51861,
  title = {{Differentially Private Range Subgraph Counting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=QYpByrxSTg},
}

@inproceedings{neurips2025_differentially_75420,
  title = {{Differentially Private Gomory-Hu Trees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=7rBeyE4nie},
}

@inproceedings{neurips2025_private_77222,
  title = {{Private Set Union with Multiple Contributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=RdNYp8ilPr},
}

@inproceedings{iclr2025_on_84094,
  title = {{On the Price of Differential Privacy for Hierarchical Clustering}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27734},
}

@inproceedings{neurips2026_online_91686,
  title = {{Online Differentially Private Consistent Clustering}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/151530},
}

@inproceedings{neurips2026_publishing_91767,
  title = {{Publishing Below-Threshold Triangle Counts under Local Weight Differential Privacy}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149634},
}

@inproceedings{neurips2026_lumberjack_94966,
  title = {{Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/148332},
}

@inproceedings{aaai2026_improved_106444,
  title = {{Improved Differentially Private Algorithms for Rank Aggregation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38751},
}

@inproceedings{aaai2026_topological_107230,
  title = {{Topological Federated Clustering via Gravitational Potential Fields Under Local Differential Privacy}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39582},
}