{"external_id":{"arxiv":["2406.11649"]},"abstract":[{"text":"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.","lang":"eng"}],"_id":"18116","date_updated":"2024-10-01T08:27:40Z","article_processing_charge":"No","title":"Making old things new: A unified algorithm for differentially private clustering","publication_status":"published","citation":{"ama":"La Tour MD, Henzinger MH, 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.","chicago":"La Tour, Max Dupré, Monika H 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.","short":"M.D. La Tour, M.H. Henzinger, D. Saulpic, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 12046–12086.","ieee":"M. D. La Tour, M. H. 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.","ista":"La Tour MD, Henzinger MH, 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.","mla":"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.","apa":"La Tour, M. D., Henzinger, M. H., &#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."},"date_published":"2024-09-01T00:00:00Z","status":"public","month":"09","author":[{"last_name":"La Tour","first_name":"Max Dupré","full_name":"La Tour, Max Dupré"},{"full_name":"Henzinger, Monika H","id":"540c9bbd-f2de-11ec-812d-d04a5be85630","first_name":"Monika H","last_name":"Henzinger","orcid":"0000-0002-5008-6530"},{"first_name":"David","last_name":"Saulpic","full_name":"Saulpic, David","id":"f8e48cf0-b0ff-11ed-b0e9-b4c35598f964"}],"language":[{"iso":"eng"}],"year":"2024","corr_author":"1","ec_funded":1,"scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","department":[{"_id":"MoHe"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2406.11649"}],"day":"01","publisher":"ML Research Press","publication":"Proceedings of the 41st International Conference on Machine Learning","publication_identifier":{"eissn":["2640-3498"]},"quality_controlled":"1","oa":1,"page":"12046-12086","conference":{"name":"ICML: International Conference on Machine Learning","location":"Vienna, Austria","end_date":"2024-07-27","start_date":"2024-07-21"},"volume":235,"type":"conference","date_created":"2024-09-22T22:01:44Z","acknowledgement":"Monika Henzinger: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 101019564) and the Austrian Science Fund (FWF) grant DOI 10.55776/Z422, grant DOI 10.55776/I5982, and grant DOI 10.55776/P33775 with additional funding from the netidee SCIENCE Stiftung, 2020–2024.This work was partially done while David Saulpic was at the Institute for Science and Technology, Austria (ISTA). David Sauplic has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101034413.","intvolume":"       235","alternative_title":["PMLR"],"project":[{"call_identifier":"H2020","_id":"bd9ca328-d553-11ed-ba76-dc4f890cfe62","name":"The design and evaluation of modern fully dynamic data structures","grant_number":"101019564"},{"_id":"34def286-11ca-11ed-8bc3-da5948e1613c","grant_number":"Z00422","name":"Wittgenstein Award - Monika Henzinger"},{"_id":"bda196b2-d553-11ed-ba76-8e8ee6c21103","grant_number":"I05982","name":"Static and Dynamic Hierarchical Graph Decompositions"},{"grant_number":"P33775 ","name":"Fast Algorithms for a Reactive Network Layer","_id":"bd9e3a2e-d553-11ed-ba76-8aa684ce17fe"},{"call_identifier":"H2020","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","grant_number":"101034413","name":"IST-BRIDGE: International postdoctoral program"}]}