{"oa":1,"article_type":"original","status":"public","language":[{"iso":"eng"}],"PlanS_conform":"1","_id":"22318","date_updated":"2026-07-16T09:14:49Z","publication_identifier":{"issn":["2836-6573"]},"arxiv":1,"ddc":["000"],"researchdata_availability":"no","project":[{"_id":"bd9ca328-d553-11ed-ba76-dc4f890cfe62","name":"The design and evaluation of modern fully dynamic data structures","call_identifier":"H2020","grant_number":"101019564"},{"grant_number":"I05982","_id":"bda196b2-d553-11ed-ba76-8e8ee6c21103","name":"Static and Dynamic Hierarchical Graph Decompositions"},{"grant_number":"P33775","name":"Fast Algorithms for a Reactive Network Layer","_id":"bd9e3a2e-d553-11ed-ba76-8aa684ce17fe"},{"grant_number":"Z00422","name":"Efficient algorithms","_id":"34def286-11ca-11ed-8bc3-da5948e1613c"}],"file":[{"creator":"dernst","content_type":"application/pdf","file_id":"22345","file_size":655405,"date_created":"2026-07-16T09:09:53Z","file_name":"2026_ACMMgmtData_Henzinger.pdf","access_level":"open_access","success":1,"relation":"main_file","checksum":"c6c5e256d02b90682c0690c3bee94040","date_updated":"2026-07-16T09:09:53Z"}],"OA_place":"publisher","department":[{"_id":"MoHe"}],"ec_funded":1,"external_id":{"arxiv":["2411.03299"]},"supplementarymaterial":"no","OA_type":"gold","acknowledgement":"1Salil Vadhan was supported by NSF grant BCS-2218803, a grant from the Sloan Foundation, and\r\na Simons Investigator Award. Work began while a Visiting Researcher at the Bocconi University\r\nDepartment of Computing Sciences, supported by Luca Trevisan’s ERC Project GA-834861.\r\n2Monika Henzinger and Roodabeh Safavi were supported by the European Research Council (ERC)\r\nunder the European Union’s Horizon 2020 research and innovation programme (Grant agreement\r\nNo. 101019564), and the Austrian Science Fund (FWF) under grants DOI 10.55776/Z422, DOI\r\n10.55776/I5982, and DOI 10.55776/P33775. For open access purposes, the author has applied a CC BY\r\npublic copyright license to any author-accepted manuscript version arising from this submission.\r\nViews and opinions expressed are however those of the author(s)\r\nonly and do not necessarily reflect those of the European Union\r\nor the European Research Council Executive Agency. Neither the\r\nEuropean Union nor the granting authority can be held responsible for them.","oa_version":"Published Version","type":"journal_article","das_tickbox":"0","year":"2026","page":"1-26","volume":4,"month":"06","issue":"2","publisher":"Association for Computing Machinery","doi":"10.1145/3801895","has_accepted_license":"1","date_published":"2026-06-01T00:00:00Z","day":"01","corr_author":"1","title":"Concurrent composition for differentially private continual mechanisms","intvolume":"         4","publication":"Proceedings of the ACM on Management of Data","quality_controlled":"1","file_date_updated":"2026-07-16T09:09:53Z","keyword":["differential privacy","concurrent composition","continual release","continual observation","data streaming","continual mechanisms","concurrent parallel composition","concurrent filter composition"],"author":[{"orcid":"0000-0002-5008-6530","id":"540c9bbd-f2de-11ec-812d-d04a5be85630","last_name":"Henzinger","full_name":"Henzinger, Monika H","first_name":"Monika H"},{"first_name":"Roodabeh","id":"72ed2640-8972-11ed-ae7b-f9c81ec75154","full_name":"Safavi Hemami, Roodabeh","last_name":"Safavi Hemami"},{"first_name":"Salil","full_name":"Vadhan, Salil","last_name":"Vadhan"}],"scopus_import":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","article_processing_charge":"Yes","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_created":"2026-07-13T14:59:14Z","abstract":[{"lang":"eng","text":"Many intended uses of differential privacy involve a continual mechanism that is set up to run continuously\r\nover a long period of time, making more statistical releases as either queries come in or the dataset is updated.\r\nIn this paper, we give the first general treatment of privacy against adaptive adversaries for mechanisms that\r\nsupport dataset updates and a variety of queries, all arbitrarily interleaved. It also models a very general notion\r\nof neighboring, that includes both event-level and user-level privacy. We prove several concurrent composition\r\ntheorems for continual mechanisms, which ensure privacy even when an adversary can interleave its queries\r\nand dataset updates to the different composed mechanisms. Previous concurrent composition theorems for\r\ndifferential privacy were only for the case when the dataset is static, with no adaptive updates. We also give\r\nthe first interactive and continual generalizations of the “parallel composition theorem” for noninteractive\r\ndifferential privacy. Specifically, we show that the analogue of the noninteractive parallel composition theorem\r\nholds if either there are no adaptive dataset updates or each of the composed mechanisms satisfies pure\r\ndifferential privacy, but it fails to hold for composing approximately differentially private mechanisms with\r\ndataset updates. Thus, we prove a tight new composition theorem for this case. In addition, we prove concurrent\r\nfilter compositions theorems for the scenarios in which the privacy parameters are adaptively chosen. We\r\nextend these results to other measures of differential privacy, including Rényi DP and 𝑓 -DP.\r\nWe then formalize a set of general conditions on a continual mechanism M that runs multiple continual submechanisms such that the privacy guarantees of M follow directly using the above concurrent composition\r\ntheorems on the sub-mechanisms, without further privacy loss. This enables us to give a simpler and modular\r\nprivacy analysis of a recent continual histogram mechanism of Henzinger, Sricharan, and Steiner. In the\r\ncase of approximate DP, ours is the first proof that shows that its privacy holds against adaptive adversaries.\r\nWe also provide a framework that simplifies the analysis of local differential privacy when the protocol\r\nincludes multi-round server-user interactions. Using this result, we simplify the privacy analysis of the core\r\ndecomposition protocol of Dhulipala, Henzinger, Li, Liu, Sricharan, and Zhu [5]."}],"citation":{"ama":"Henzinger M, Safavi Hemami R, Vadhan S. Concurrent composition for differentially private continual mechanisms. <i>Proceedings of the ACM on Management of Data</i>. 2026;4(2):1-26. doi:<a href=\"https://doi.org/10.1145/3801895\">10.1145/3801895</a>","short":"M. Henzinger, R. Safavi Hemami, S. Vadhan, Proceedings of the ACM on Management of Data 4 (2026) 1–26.","apa":"Henzinger, M., Safavi Hemami, R., &#38; Vadhan, S. (2026). Concurrent composition for differentially private continual mechanisms. <i>Proceedings of the ACM on Management of Data</i>. Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3801895\">https://doi.org/10.1145/3801895</a>","ista":"Henzinger M, Safavi Hemami R, Vadhan S. 2026. Concurrent composition for differentially private continual mechanisms. Proceedings of the ACM on Management of Data. 4(2), 1–26.","chicago":"Henzinger, Monika, Roodabeh Safavi Hemami, and Salil Vadhan. “Concurrent Composition for Differentially Private Continual Mechanisms.” <i>Proceedings of the ACM on Management of Data</i>. Association for Computing Machinery, 2026. <a href=\"https://doi.org/10.1145/3801895\">https://doi.org/10.1145/3801895</a>.","ieee":"M. Henzinger, R. Safavi Hemami, and S. Vadhan, “Concurrent composition for differentially private continual mechanisms,” <i>Proceedings of the ACM on Management of Data</i>, vol. 4, no. 2. Association for Computing Machinery, pp. 1–26, 2026.","mla":"Henzinger, Monika, et al. “Concurrent Composition for Differentially Private Continual Mechanisms.” <i>Proceedings of the ACM on Management of Data</i>, vol. 4, no. 2, Association for Computing Machinery, 2026, pp. 1–26, doi:<a href=\"https://doi.org/10.1145/3801895\">10.1145/3801895</a>."}}