{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T23:33:25Z","timestamp":1784244805417,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T00:00:00Z","timestamp":1672444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61872110"],"award-info":[{"award-number":["61872110"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated generative models with differential privacy are used by the existing methods. Unfortunately, the existing models apply only to images or text data and not to tabular data. Unlike images, tabular data usually consist of mixed data types (discrete and continuous attributes) and real-world datasets with highly imbalanced data distributions. Existing methods hardly model such scenarios due to the multimodal distributions in the decentralized continuous columns and highly imbalanced categorical attributes of the clients. To solve these problems, we propose a federated generative model for decentralized tabular data synthesis (HT-Fed-GAN). There are three important parts of HT-Fed-GAN: the federated variational Bayesian Gaussian mixture model (Fed-VB-GMM), which is designed to solve the problem of multimodal distributions; federated conditional one-hot encoding with conditional sampling for global categorical attribute representation and rebalancing; and a privacy consumption-based federated conditional GAN for privacy-preserving decentralized data modeling. The experimental results on five real-world datasets show that HT-Fed-GAN obtains the best trade-off between the data utility and privacy level. For the data utility, the tables generated by HT-Fed-GAN are the most statistically similar to the original tables and the evaluation scores show that HT-Fed-GAN outperforms the state-of-the-art model in terms of machine learning tasks.<\/jats:p>","DOI":"10.3390\/e25010088","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T04:51:22Z","timestamp":1672635082000},"page":"88","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["HT-Fed-GAN: Federated Generative Model for Decentralized Tabular Data Synthesis"],"prefix":"10.3390","volume":"25","author":[{"given":"Shaoming","family":"Duan","sequence":"first","affiliation":[{"name":"School of Computer Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"},{"name":"Insititute of Data Security, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanyi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"},{"name":"Insititute of Data Security, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"},{"name":"Peng Cheng Laboratory, Department of New Networks, Shenzhen 518000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiyi","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"},{"name":"Insititute of Data Security, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"},{"name":"Peng Cheng Laboratory, Department of New Networks, Shenzhen 518000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopeng","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7144-5037","authenticated-orcid":false,"given":"Xinyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, The Chinese University of Hong Kong, Shenzhen 518172, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyu","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"},{"name":"Insititute of Data Security, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hezhong","family":"Pan","sequence":"additional","affiliation":[{"name":"Peng Cheng Laboratory, Department of New Networks, Shenzhen 518000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiayu","family":"Xiang","sequence":"additional","affiliation":[{"name":"Peng Cheng Laboratory, Department of New Networks, Shenzhen 518000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,31]]},"reference":[{"key":"ref_1","first-page":"202","article-title":"Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid","volume":"96","author":"Kohavi","year":"1996","journal-title":"KDD 1996 Proceedings"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"McFee, B., Bertin-Mahieux, T., Ellis, D.P., and Lanckriet, G.R. 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