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Differentially Private Hierarchical Clustering with Provable Approximation Guarantees.

, , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 14353-14375. PMLR, (2023)

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Locally Differentially Private Analysis of Graph Statistics., , and . CoRR, (2020)Privacy Amplification Via Bernoulli Sampling., and . CoRR, (2021)No-Substitution $k$-means Clustering with Low Center Complexity and Memory., and . CoRR, (2021)Robustness of Locally Differentially Private Graph Analysis Against Poisoning., , and . CoRR, (2022)Communication-Efficient Triangle Counting under Local Differential Privacy., , and . USENIX Security Symposium, page 537-554. USENIX Association, (2022)Private estimation algorithms for stochastic block models and mixture models., , , , , , and . CoRR, (2023)Balancing utility and scalability in metric differential privacy., , , , and . UAI, volume 180 of Proceedings of Machine Learning Research, page 885-894. PMLR, (2022)Capacity Bounded Differential Privacy., , and . NeurIPS, page 3469-3478. (2019)Differentially Private Hierarchical Clustering with Provable Approximation Guarantees., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 14353-14375. PMLR, (2023)Differentially Private Subgraph Counting in the Shuffle Model., , and . CoRR, (2022)