conference paper published yes Max Dupré La Tour author Monika H Henzinger author 540c9bbd-f2de-11ec-812d-d04a5be856300000-0002-5008-6530 David Saulpic author f8e48cf0-b0ff-11ed-b0e9-b4c35598f964 MoHe department ICML: International Conference on Machine Learning The design and evaluation of modern fully dynamic data structures project Efficient algorithms project Static and Dynamic Hierarchical Graph Decompositions project Fast Algorithms for a Reactive Network Layer project IST-BRIDGE: International postdoctoral program project As a staple of data analysis and unsupervised learning, the problem of private clustering has been widely studied, under various privacy models. Centralized differential privacy is the first of them, and the problem has also been studied for the local and the shuffle variation. In each case, the goal is to design an algorithm that computes privately a clustering, with the smallest possible error. The study of each variation gave rise to new algorithm: the landscape of private clustering algorithm is therefore quite intricate. In this paper, we show that a 20 year-old algorithm can be slightly modified to work for any of those models. This provides a unified picture: while matching almost all previously known results, it allows us to improve some of them, and extend to a new privacy model, the continual observation setting, where the input is changing over time and the algorithm must output a new solution at each time step. ML Research Press2024Vienna, Austria eng 2640-3498 2406.11649 23512046-12086 La Tour MD, Henzinger M, Saulpic D. 2024. Making old things new: A unified algorithm for differentially private clustering. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 12046–12086. M.D. La Tour, M. Henzinger, D. Saulpic, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 12046–12086. La Tour, Max Dupré, et al. “Making Old Things New: A Unified Algorithm for Differentially Private Clustering.” <i>Proceedings of the 41st International Conference on Machine Learning</i>, vol. 235, ML Research Press, 2024, pp. 12046–86. La Tour, Max Dupré, Monika Henzinger, and David Saulpic. “Making Old Things New: A Unified Algorithm for Differentially Private Clustering.” In <i>Proceedings of the 41st International Conference on Machine Learning</i>, 235:12046–86. ML Research Press, 2024. La Tour MD, Henzinger M, Saulpic D. Making old things new: A unified algorithm for differentially private clustering. In: <i>Proceedings of the 41st International Conference on Machine Learning</i>. Vol 235. ML Research Press; 2024:12046-12086. La Tour, M. D., Henzinger, M., &#38; Saulpic, D. (2024). Making old things new: A unified algorithm for differentially private clustering. In <i>Proceedings of the 41st International Conference on Machine Learning</i> (Vol. 235, pp. 12046–12086). Vienna, Austria: ML Research Press. M. D. La Tour, M. Henzinger, and D. Saulpic, “Making old things new: A unified algorithm for differentially private clustering,” in <i>Proceedings of the 41st International Conference on Machine Learning</i>, Vienna, Austria, 2024, vol. 235, pp. 12046–12086. 181162024-09-22T22:01:44Z2025-04-14T13:50:50Z