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ACM Manag. Data"],"published-print":{"date-parts":[[2026,5,18]]},"abstract":"<jats:p>Traditional Differential Privacy (DP) mechanisms are typically tailored to specific analysis tasks, which limits the reusability of protected data. DP tabular data synthesis overcomes this by generating synthetic datasets that can be shared for arbitrary downstream tasks. However, existing synthesis methods predominantly assume centralized or local settings and overlook the more practical horizontal federated scenario. Na\u00efvely synthesizing data locally or perturbing individual records either produces biased mixtures or introduces excessive noise, especially under heterogeneous data distributions across participants.<\/jats:p>\n                  <jats:p>\n                    We propose HeteroFedSyn, the first DP tabular data synthesis framework designed specifically for the horizontal federated setting. Built upon the PrivSyn paradigm of 2-way marginal\u2013based synthesis, HeteroFedSyn introduces three key innovations for distributed marginal selection: (i) an\n                    <jats:italic toggle=\"yes\">l<\/jats:italic>\n                    <jats:sub>2<\/jats:sub>\n                    -based dependency metric with random projection for noise-efficient correlation measurement, (ii) an unbiased estimator to correct multiplicative noise, and (iii) an adaptive selection strategy that dynamically updates dependency scores to avoid redundancy. Extensive experiments on range queries, Wasserstein fidelity, and machine learning tasks show that, despite the increased noise inherent to federated execution, HeteroFedSyn achieves utility comparable to centralized synthesis. Our code is open-sourced via the link.\n                  <\/jats:p>","DOI":"10.1145\/3802072","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:19:16Z","timestamp":1779128356000},"page":"1-26","source":"Crossref","is-referenced-by-count":0,"title":["HeteroFedSyn: Differentially Private Tabular Data Synthesis for Heterogeneous Federated Settings"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3722-4783","authenticated-orcid":false,"given":"Xiaochen","family":"Li","sequence":"first","affiliation":[{"name":"UNC Greensboro, Greensboro, North Carolina, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0274-9486","authenticated-orcid":false,"given":"Fengyu","family":"Gao","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2561-3076","authenticated-orcid":false,"given":"Xizixiang","family":"Wei","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9017-7947","authenticated-orcid":false,"given":"Tianhao","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3148-4453","authenticated-orcid":false,"given":"Cong","family":"Shen","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6009-864X","authenticated-orcid":false,"given":"Jing","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2021. 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Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. In Theory of Cryptography - 14th International Conference, TCC 2016-B, Beijing, China, October 31 - November 3, 2016, Proceedings, Part I (Lecture Notes in Computer Science, Vol. 9985), Martin Hirt and Adam D. Smith (Eds.). 635-658."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476272"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3769764"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783379"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_1_16_1","first-page":"218","volume-title":"11th International Conference, CANS 2012, Darmstadt, Germany, December 12-14, 2012. 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Systematic assessment of tabular data synthesis algorithms. arXiv preprint arXiv:2402.06806 (2024)."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/e25010088"},{"key":"e_1_2_1_21_1","volume-title":"33rd International Colloquium, ICALP 2006, Venice, Italy, July 10-14, 2006, Proceedings, Part II (Lecture Notes in Computer Science","volume":"12","author":"Dwork Cynthia","year":"2006","unstructured":"Cynthia Dwork. 2006. Differential Privacy. In Automata, Languages and Programming, 33rd International Colloquium, ICALP 2006, Venice, Italy, July 10-14, 2006, Proceedings, Part II (Lecture Notes in Computer Science, Vol. 4052), Michele Bugliesi, Bart Preneel, Vladimiro Sassone, and Ingo Wegener (Eds.). 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PATE-GAN: Generating synthetic data with differential privacy guarantees. In International conference on learning representations."},{"key":"e_1_2_1_31_1","article-title":"Privacy via the Johnson-Lindenstrauss Transform","volume":"5","author":"Kenthapadi Krishnaram","year":"2013","unstructured":"Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, and Nina Mishra. 2013. Privacy via the Johnson-Lindenstrauss Transform. J. Priv. Confidentiality, Vol. 5, 1 (2013).","journal-title":"J. Priv. Confidentiality"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-34621-8_23"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3300102"},{"key":"e_1_2_1_34_1","volume-title":"A data-and workload-aware algorithm for range queries under differential privacy. arXiv preprint arXiv:1410.0265","author":"Li Chao","year":"2014","unstructured":"Chao Li, Michael Hay, Gerome Miklau, and Yue Wang. 2014. A data-and workload-aware algorithm for range queries under differential privacy. arXiv preprint arXiv:1410.0265 (2014)."},{"key":"e_1_2_1_35_1","volume-title":"2025 IEEE Symposium on Security and Privacy (SP). IEEE, 3988-4006","author":"Li Kecen","year":"2025","unstructured":"Kecen Li, Chen Gong, Xiaochen Li, Yuzhong Zhao, Xinwen Hou, and Tianhao Wang. 2025a. From easy to hard: Building a shortcut for differentially private image synthesis. In 2025 IEEE Symposium on Security and Privacy (SP). IEEE, 3988-4006."},{"key":"e_1_2_1_36_1","volume-title":"Differential Privacy with Random Projections and Sign Random Projections. CoRR","author":"Li Ping","year":"2023","unstructured":"Ping Li and Xiaoyun Li. 2023. Differential Privacy with Random Projections and Sign Random Projections. CoRR, Vol. abs\/2306.01751 (2023)."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3714420"},{"key":"e_1_2_1_38_1","volume-title":"Delay-allowed Differentially Private Data Stream Release. 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IEEE, 2300-2307."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1090\/conm\/026\/737400"},{"key":"e_1_2_1_41_1","volume-title":"Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems","author":"Liu Terrance","year":"2021","unstructured":"Terrance Liu, Giuseppe Vietri, and Steven Wu. 2021. Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 690-702."},{"key":"e_1_2_1_42_1","article-title":"Winning the NIST Contest: A scalable and general approach to differentially private synthetic data","volume":"11","author":"McKenna Ryan","year":"2021","unstructured":"Ryan McKenna, Gerome Miklau, and Daniel Sheldon. 2021. Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. J. Priv. Confidentiality, Vol. 11, 3 (2021).","journal-title":"J. Priv. Confidentiality"},{"key":"e_1_2_1_43_1","volume-title":"Proceedings of the VLDB Endowment","volume":"15","author":"McKenna Ryan","year":"2022","unstructured":"Ryan McKenna, Brett Mullins, Daniel Sheldon, and Gerome Miklau. 2022. AIM: an adaptive and iterative mechanism for differentially private synthetic data. Proceedings of the VLDB Endowment, Vol. 15, 11 (2022)."},{"key":"e_1_2_1_44_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML 2019","volume":"4444","author":"McKenna Ryan","year":"2019","unstructured":"Ryan McKenna, Daniel Sheldon, and Gerome Miklau. 2019. Graphical-model based estimation and inference for differential privacy. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 4435-4444."},{"key":"e_1_2_1_45_1","volume-title":"Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation. arXiv preprint arXiv:2412.16083","author":"Sattarov Timur","year":"2024","unstructured":"Timur Sattarov, Marco Schreyer, and Damian Borth. 2024. Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation. arXiv preprint arXiv:2412.16083 (2024)."},{"key":"e_1_2_1_46_1","volume-title":"2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 205-216","author":"Su Sen","year":"2016","unstructured":"Sen Su, Peng Tang, Xiang Cheng, Rui Chen, and Zequn Wu. 2016. Differentially private multi-party high-dimensional data publishing. In 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 205-216."},{"key":"e_1_2_1_47_1","volume-title":"Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning. CoRR","author":"Such Felipe Petroski","year":"2017","unstructured":"Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O. Stanley, and Jeff Clune. 2017. Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning. CoRR, Vol. abs\/1712.06567 (2017)."},{"key":"e_1_2_1_48_1","article-title":"Sparse Projection Oblique Randomer Forests","volume":"21","author":"Tomita Tyler M.","year":"2020","unstructured":"Tyler M. Tomita, James Browne, Cencheng Shen, Jaewon Chung, Jesse Patsolic, Benjamin Falk, Carey E. Priebe, Jason Yim, Randal C. Burns, Mauro Maggioni, and Joshua T. Vogelstein. 2020. Sparse Projection Oblique Randomer Forests. J. Mach. Learn. Res., Vol. 21 (2020), 104:1-104:39.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00018"},{"key":"e_1_2_1_50_1","volume-title":"Differentially private tabular data synthesis using large language models. arXiv preprint arXiv:2406.01457","author":"Tran Toan V","year":"2024","unstructured":"Toan V Tran and Li Xiong. 2024. Differentially private tabular data synthesis using large language models. arXiv preprint arXiv:2406.01457 (2024)."},{"key":"e_1_2_1_51_1","volume-title":"Generating tabular datasets under differential privacy. arXiv preprint arXiv:2308.14784","author":"Truda Gianluca","year":"2023","unstructured":"Gianluca Truda. 2023. Generating tabular datasets under differential privacy. arXiv preprint arXiv:2308.14784 (2023)."},{"key":"e_1_2_1_52_1","volume-title":"Differentially private generative adversarial network. arXiv preprint arXiv:1802.06739","author":"Xie Liyang","year":"2018","unstructured":"Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou. 2018. Differentially private generative adversarial network. arXiv preprint arXiv:1802.06739 (2018)."},{"key":"e_1_2_1_53_1","volume-title":"Tabular data synthesis with differential privacy: A survey. arXiv preprint arXiv:2411.03351","author":"Yang Mengmeng","year":"2024","unstructured":"Mengmeng Yang, Chi-Hung Chi, Kwok-Yan Lam, Jie Feng, Taolin Guo, and Wei Ni. 2024. Tabular data synthesis with differential privacy: A survey. arXiv preprint arXiv:2411.03351 (2024)."},{"key":"e_1_2_1_54_1","volume-title":"International Conference on Management of Data, SIGMOD 2014","author":"Zhang Jun","year":"2014","unstructured":"Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao. 2014. PrivBayes: private data release via bayesian networks. In International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA, June 22-27, 2014, Curtis E. Dyreson, Feifei Li, and M. Tamer \u00d6zsu (Eds.). ACM, 1423-1434."},{"key":"e_1_2_1_55_1","first-page":"929","volume-title":"PrivSyn: Differentially Private Data Synthesis. In 30th USENIX Security Symposium, USENIX Security 2021","author":"Zhang Zhikun","year":"2021","unstructured":"Zhikun Zhang, Tianhao Wang, Ninghui Li, Jean Honorio, Michael Backes, Shibo He, Jiming Chen, and Yang Zhang. 2021. PrivSyn: Differentially Private Data Synthesis. In 30th USENIX Security Symposium, USENIX Security 2021, August 11-13, 2021, Michael D. Bailey and Rachel Greenstadt (Eds.). USENIX Association, 929-946."},{"key":"e_1_2_1_56_1","volume-title":"Fed-tgan: Federated learning framework for synthesizing tabular data. arXiv preprint arXiv:2108.07927","author":"Zhao Zilong","year":"2021","unstructured":"Zilong Zhao, Robert Birke, Aditya Kunar, and Lydia Y Chen. 2021. Fed-tgan: Federated learning framework for synthesizing tabular data. arXiv preprint arXiv:2108.07927 (2021)."},{"key":"e_1_2_1_57_1","doi-asserted-by":"crossref","first-page":"13524","DOI":"10.1038\/s41598-021-93030-0","article-title":"Medical imaging deep learning with differential privacy","volume":"11","author":"Ziller Alexander","year":"2021","unstructured":"Alexander Ziller, Dmitrii Usynin, Rickmer Braren, Marcus Makowski, Daniel Rueckert, and Georgios Kaissis. 2021. Medical imaging deep learning with differential privacy. 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