{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T21:27:54Z","timestamp":1777152474492,"version":"3.51.4"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T00:00:00Z","timestamp":1715299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"U.S. Census Bureau","doi-asserted-by":"publisher","award":["CB20ADR0160001"],"award-info":[{"award-number":["CB20ADR0160001"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"Computing Research Association","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Computing Community Consortium"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2024,5,10]]},"abstract":"<jats:p>Motivated by privacy concerns in long-term longitudinal studies in medical and social science research, we study the problem of continually releasing differentially private synthetic data from longitudinal data collections. We introduce a model where, in every time step, each individual reports a new data element, and the goal of the synthesizer is to incrementally update a synthetic dataset in a consistent way to capture a rich class of statistical properties. We give continual synthetic data generation algorithms that preserve two basic types of queries: fixed time window queries and cumulative time queries. We show nearly tight upper bounds on the error rates of these algorithms and demonstrate their empirical performance on realistically sized datasets from the U.S. Census Bureau's Survey of Income and Program Participation.<\/jats:p>","DOI":"10.1145\/3651595","type":"journal-article","created":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T08:32:13Z","timestamp":1715675533000},"page":"1-26","source":"Crossref","is-referenced-by-count":4,"title":["Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5855-8045","authenticated-orcid":false,"given":"Mark","family":"Bun","sequence":"first","affiliation":[{"name":"Boston University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5235-7066","authenticated-orcid":false,"given":"Marco","family":"Gaboardi","sequence":"additional","affiliation":[{"name":"Boston University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9137-5785","authenticated-orcid":false,"given":"Marcel","family":"Neunhoeffer","sequence":"additional","affiliation":[{"name":"Institute for Employment Research &amp; LMU Munich, Nuremberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2393-2308","authenticated-orcid":false,"given":"Wanrong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Harvard University, Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,14]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"John Abowd et al. 2021. An uncertainty principle is a price of privacy-preserving microdata. In Advances in Neural Information Processing Systems. M. Ranzato A. Beygelzimer Y. Dauphin P. S. Liang and J. Wortman Vaughan (Eds.) Vol. 34. Curran Associates Inc. 11883--11895. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/639d79cc857a6c76c2723b7e014fccb0-Paper.pdf."},{"key":"e_1_2_1_2_1","unstructured":"Daniel Alabi Omri Ben-Eliezer and Anamay Chaturvedi. 2022. Bounded space differentially private quantiles. CoRR abs\/2201.03380."},{"key":"e_1_2_1_3_1","unstructured":"H\u00e9ber H. Arcolezi Carlos Pinz\u00f3n Catuscia Palamidessi and S\u00e9bastien Gambs. 2022. Frequency estimation of evolving data under local differential privacy. arXiv preprint arXiv:2210.00262."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1265530.1265569"},{"key":"e_1_2_1_5_1","unstructured":"Gary Benedetto Stanley Jordan C. and Totty Evan. 2018. The creation and use of the {sipp} synthetic beta v7.0. https:\/\/www.census.gov\/library\/working-papers\/2018\/adrm\/SIPP-Synthetic-Beta.html."},{"key":"e_1_2_1_6_1","volume-title":"Abowd","author":"Benedetto Gary","year":"2013","unstructured":"Gary Benedetto, Martha H. Stinson, and John M. Abowd. 2013. The creation and use of the {sipp} synthetic beta. https:\/\/www.census.gov\/content\/dam\/Census\/programs-surveys\/sipp\/methodology\/SSBdescribe%7B%5C_%7Dnontechnical.pdf."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1374376.1374464"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2448496.2448530"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-53641-4_24"},{"key":"e_1_2_1_10_1","unstructured":"U.S. Census Bureau. 2021. Longitudinal Business Database. Tech. rep. https:\/\/www.census.gov\/programs-surveys\/ces\/data\/restricted-use-data\/longitudinal-business-database.html."},{"key":"e_1_2_1_11_1","unstructured":"U.S. Census Bureau. 2021. Survey of Income and Program Participation Data. Tech. rep. https:\/\/www2.census.gov\/programs-surveys\/sipp\/data\/datasets\/2021\/pu2021_csv.zip."},{"key":"e_1_2_1_12_1","unstructured":"U.S. Census Bureau. 2023. Synthetic Longitudinal Business Database. Tech. rep. https:\/\/www.census.gov\/programs-surveys\/ces\/data\/public-use-data\/synthetic-longitudinal-business-database.html."},{"key":"e_1_2_1_13_1","first-page":"15676","article-title":"The discrete gaussian for differential privacy","volume":"33","author":"Canonne Cl\u00e9ment L.","year":"2020","unstructured":"Cl\u00e9ment L. Canonne, Gautam Kamath, and Thomas Steinke. 2020. The discrete gaussian for differential privacy. Advances in Neural Information Processing Systems, 33, 15676--15688.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_14_1","volume-title":"International Conference on Artificial Intelligence and Statistics, {AISTATS} 2022","volume":"151","author":"Cardoso Adrian Rivera","year":"2022","unstructured":"Adrian Rivera Cardoso and Ryan Rogers. 2022. Differentially private histograms under continual observation: streaming selection into the unknown. In International Conference on Artificial Intelligence and Statistics, {AISTATS} 2022, 28--30 March 2022, Virtual Event (Proceedings of Machine Learning Research). Gustau Camps-Valls, Francisco J. R. Ruiz, and Isabel Valera, (Eds.) Vol. 151. {PMLR}, 2397--2419."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2043621.2043626"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134102"},{"key":"e_1_2_1_17_1","volume-title":"J. Keith Rush, Adam Smith, and Abhradeep Guha Thakurta.","author":"Denissov Serguei","year":"2022","unstructured":"Serguei Denissov, Hugh Brendan McMahan, J. Keith Rush, Adam Smith, and Abhradeep Guha Thakurta. 2022. Improved differential privacy for {sgd} via optimal private linear operators on adaptive streams. In Advances in Neural Information Processing Systems. Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, (Eds.) https:\/\/openreview.net\/forum?id=i9XrHJoyLqJ."},{"key":"e_1_2_1_18_1","volume-title":"Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017","author":"Ding Bolin","year":"2017","unstructured":"Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. 2017. Collecting telemetry data privately. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, {USA}. Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, (Eds.), 3571--3580."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.2.5001.1071"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the 42nd ACM Symposium on Theory of Computing (STOC '10)","author":"Dwork Cynthia","unstructured":"Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N. Rothblum. 2010. Differential privacy under continual observation. In Proceedings of the 42nd ACM Symposium on Theory of Computing (STOC '10), 715--724."},{"key":"e_1_2_1_22_1","volume-title":"Rothblum","author":"Dwork Cynthia","year":"2016","unstructured":"Cynthia Dwork and Guy N. Rothblum. 2016. Concentrated differential privacy. arXiv preprint arXiv:1603.01887."},{"key":"e_1_2_1_23_1","volume-title":"14th Innovations in Theoretical Computer Science Conference, {ITCS} 2023","author":"Epasto Alessandro","year":"2023","unstructured":"Alessandro Epasto, Jieming Mao, Andres Mu\u00f1oz Medina, Vahab Mirrokni, Sergei Vassilvitskii, and Peilin Zhong. 2023. Differentially private continual releases of streaming frequency moment estimations. In 14th Innovations in Theoretical Computer Science Conference, {ITCS} 2023, January 10--13, 2023, MIT, Cambridge, Massachusetts, {USA} (LIPIcs). Yael Tauman Kalai, (Ed.) Vol. 251. Schloss Dagstuhl - Leibniz-Zentrum f\u00fcr Informatik, 48:1--48:24."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975482.151"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660348"},{"key":"e_1_2_1_26_1","unstructured":"Hendrik Fichtenberger Monika Henzinger and Jalaj Upadhyay. 2022. Constant matters: fine-grained complexity of differentially private continual observation. (2022)."},{"key":"e_1_2_1_27_1","volume-title":"Proceedings of the 31th International Conference on Machine Learning, {ICML} 2014, Beijing, China, 21--26 June 2014 ({JMLR} Workshop and Conference Proceedings).","volume":"32","author":"Gaboardi Marco","year":"2014","unstructured":"Marco Gaboardi, Emilio Jes\u00fas Gallego Arias, Justin Hsu, Aaron Roth, and Zhiwei Steven Wu. 2014. Dual query: practical private query release for high dimensional data. In Proceedings of the 31th International Conference on Machine Learning, {ICML} 2014, Beijing, China, 21--26 June 2014 ({JMLR} Workshop and Conference Proceedings). Vol. 32. JMLR.org, 1170--1178. http:\/\/proceedings.mlr.press\/v32\/gaboardi14.html."},{"key":"e_1_2_1_28_1","unstructured":"Badih Ghazi Ravi Kumar Pasin Manurangsi and Jelani Nelson. 2022. Private counting of distinct and k-occurring items in time windows. arXiv preprint arXiv:2211.11718."},{"key":"e_1_2_1_29_1","volume-title":"Rothblum","author":"Hardt Moritz","year":"2010","unstructured":"Moritz Hardt and Guy N. Rothblum. 2010. A multiplicative weights mechanism for privacy-preserving data analysis. In 2010 IEEE 51st annual symposium on foundations of computer science. IEEE, 61--70."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920970"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977554.ch183"},{"key":"e_1_2_1_32_1","unstructured":"James Honaker. 2015. Efficient use of differentially private binary trees. In."},{"key":"e_1_2_1_33_1","volume-title":"Smith","author":"Jain Palak","year":"2021","unstructured":"Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar, and Adam D. Smith. 2021. The price of differential privacy under continual observation. CoRR, abs\/2112.00828."},{"key":"e_1_2_1_34_1","volume-title":"June 25--27","author":"Jain Prateek","year":"2012","unstructured":"Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta. 2012. Differentially private online learning. In {COLT} 2012 - The 25th Annual Conference on Learning Theory, June 25--27, 2012, Edinburgh, Scotland ({JMLR} Proceedings). Shie Mannor, Nathan Srebro, and Robert C. Williamson, (Eds.) Vol. 23. JMLR.org, 24.1--24.34."},{"key":"e_1_2_1_35_1","unstructured":"Matthew Joseph Aaron Roth Jonathan Ullman and Bo Waggoner. 2018. Local differential privacy for evolving data. Advances in Neural Information Processing Systems 31."},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-55-1-33"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732977.2732989"},{"key":"e_1_2_1_38_1","volume-title":"Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research)","author":"Liu Terrance","year":"2023","unstructured":"Terrance Liu, Jingwu Tang, Giuseppe Vietri, and Steven Wu. 2023. Generating private synthetic data with genetic algorithms. In Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research). Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett, (Eds.) Vol. 202. PMLR, (23--29 Jul 2023), 22009--22027. https:\/\/proceedings.mlr.press\/v202\/liu23ag.html."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.778"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551817"},{"key":"e_1_2_1_41_1","volume-title":"Proceedings of the 30th {ACM} {SIGMOD-SIGACT-SIGART} Symposium on Principles of Database Systems, {PODS} 2011","author":"Mir Darakhshan J.","year":"2011","unstructured":"Darakhshan J. Mir, S. Muthukrishnan, Aleksandar Nikolov, and Rebecca N. Wright. 2011. Pan-private algorithms via statistics on sketches. In Proceedings of the 30th {ACM} {SIGMOD-SIGACT-SIGART} Symposium on Principles of Database Systems, {PODS} 2011, June 12--16, 2011, Athens, Greece. Maurizio Lenzerini and Thomas Schwentick, (Eds.) {ACM}, 37--48."},{"key":"e_1_2_1_42_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=6isfR3JCbi.","author":"Neunhoeffer Marcel","year":"2021","unstructured":"Marcel Neunhoeffer, Steven Wu, and Cynthia Dwork. 2021. Private post-gan boosting. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=6isfR3JCbi."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2488608.2488652"},{"key":"e_1_2_1_44_1","volume-title":"Randomize the future: asymptotically optimal locally private frequency estimation protocol for longitudinal data. In {PODS} '22: International Conference on Management of Data","author":"Ohrimenko Olga","year":"2022","unstructured":"Olga Ohrimenko, Anthony Wirth, and Hao Wu. 2022. Randomize the future: asymptotically optimal locally private frequency estimation protocol for longitudinal data. In {PODS} '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022. Leonid Libkin and Pablo Barcel\u00f3, (Eds.) {ACM}, 237--249."},{"key":"e_1_2_1_45_1","unstructured":"Omar Rivasplata. 2012. Subgaussian random variables: an expository note. Available at: http:\/\/www.stat.cmu.edu\/arinaldo\/36788\/subgaussians.pdf."},{"key":"e_1_2_1_46_1","unstructured":"Shuang Song Susan Little Sanjay Mehta Staal A. Vinterbo and Kamalika Chaudhuri. 2018. Differentially private continual release of graph statistics. CoRR abs\/1809.02575."},{"key":"e_1_2_1_47_1","volume-title":"Theory of Cryptography","author":"Ullman Jonathan","year":"1957","unstructured":"Jonathan Ullman and Salil Vadhan. 2011. Pcps and the hardness of generating private synthetic data. In Theory of Cryptography. Yuval Ishai, (Ed.) Springer Berlin Heidelberg, Berlin, Heidelberg, 400--416. isbn: 978--3--642--19571--6."},{"key":"e_1_2_1_48_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18","volume":"119","author":"Vietri Giuseppe","year":"2020","unstructured":"Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, and Zhiwei Steven Wu. 2020. New oracle-efficient algorithms for private synthetic data release. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18 July 2020, Virtual Event (Proceedings of Machine Learning Research). Vol. 119. PMLR, 9765--9774. http:\/\/proceedings.mlr.press\/v119\/vietri20b.html."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.3233\/SJI-160964"},{"key":"e_1_2_1_50_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=neu9JlNweE.","author":"Wang Hao","year":"2023","unstructured":"Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald, and Akash Srivastava. 2023. Post-processing private synthetic data for improving utility on selected measures. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=neu9JlNweE."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3177721"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3134428"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3651595","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3651595","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T21:40:22Z","timestamp":1755898822000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3651595"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,10]]},"references-count":52,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,5,10]]}},"alternative-id":["10.1145\/3651595"],"URL":"https:\/\/doi.org\/10.1145\/3651595","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,10]]}}}