{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T10:29:09Z","timestamp":1769768949112,"version":"3.49.0"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032159892","type":"print"},{"value":"9783032159908","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-15990-8_2","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T19:59:25Z","timestamp":1769716765000},"page":"19-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Dimensional Comparative Study of\u00a0Generative Adversarial Networks, Diffusion Models, and\u00a0Statistical Methods for\u00a0Synthetic Health Data Generation"],"prefix":"10.1007","author":[{"given":"Oluwatoyin Joy","family":"Omole","sequence":"first","affiliation":[]},{"given":"Celso","family":"Fran\u00e7a","sequence":"additional","affiliation":[]},{"given":"Samuel N.","family":"Alves","sequence":"additional","affiliation":[]},{"given":"Regina","family":"Bernal","sequence":"additional","affiliation":[]},{"given":"Deborah","family":"Malta","sequence":"additional","affiliation":[]},{"given":"Marcos Andr\u00e9","family":"Gon\u00e7alves","sequence":"additional","affiliation":[]},{"given":"Jussara M.","family":"Almeida","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS \u201916, pp. 308\u2013318. Association for Computing Machinery, New York, NY, USA (2016)","DOI":"10.1145\/2976749.2978318"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Bernal, R.T.I., et al.: A methodology for small area prevalence estimation based on survey data. Int. J. Equity Health 19, 1\u201310 (2020)","DOI":"10.1186\/s12939-020-01220-5"},{"key":"2_CR3","doi-asserted-by":"publisher","first-page":"701","DOI":"10.5123\/S1679-49742017000400003","volume":"26","author":"RTI Bernal","year":"2017","unstructured":"Bernal, R.T.I., Iser, B.P.M., Malta, D.C., Claro, R.M.: Surveillance system for risk and protective factors for chronic diseases by telephone survey (Vigitel): changes in weighting methodology. Epidemiologia e Servi\u00e7os de Sa\u00fade 26, 701\u2013712 (2017)","journal-title":"Epidemiologia e Servi\u00e7os de Sa\u00fade"},{"key":"2_CR4","unstructured":"van Breugel, B., Crabb\u00e9, J., Davis, R., van\u00a0der Schaar, M.: LaTable: towards large tabular models. arXiv preprint arXiv:2406.17673 (2024)"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"2_CR6","unstructured":"Choi, E., Biswal, S., Malin, B., Duke, J., Stewart, W.F., Sun, J.: Generating multi-label discrete patient records using generative adversarial networks. In: Machine Learning for Healthcare Conference, pp. 286\u2013305. PMLR (2017)"},{"key":"2_CR7","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2_CR8","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol.\u00a027 (2014)"},{"key":"2_CR9","unstructured":"Grinsztajn, L., Oyallon, E., Varoquaux, G.: Why do tree-based models still outperform deep learning on typical tabular data? In: Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS \u201922 (2022)"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Hittmeir, M., Mayer, R., Ekelhart, A.: A baseline for attribute disclosure risk in synthetic data. In: Proceedings of the Tenth ACM Conference on Data and Application Security and Privacy, CODASPY \u201920, pp. 133\u2013143 (2020)","DOI":"10.1145\/3374664.3375722"},{"key":"2_CR11","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol.\u00a033 (2020)"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Kaur, D., et al.: Application of Bayesian networks to generate synthetic health data. J. Am. Med. Inform. Assoc. 28(4), 801\u2013811 (2021)","DOI":"10.1093\/jamia\/ocaa303"},{"issue":"2","key":"2_CR13","first-page":"179","volume":"61","author":"P Kumar","year":"2007","unstructured":"Kumar, P., Shoukri, M.M.: Copula functions for modelling dependence structure with applications in the analysis of clinical data. J. Indian Soc. Agric. Statist. 61(2), 179\u2013191 (2007)","journal-title":"J. Indian Soc. Agric. Statist."},{"issue":"4","key":"2_CR14","first-page":"641","volume":"12","author":"J Liu","year":"2012","unstructured":"Liu, J., Luan, Y.: Copula-based statistical models for financial time series. Quant. Financ. 12(4), 641\u2013655 (2012)","journal-title":"Quant. Financ."},{"key":"2_CR15","unstructured":"Mirza, M.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"2_CR16","doi-asserted-by":"publisher","first-page":"100546","DOI":"10.1016\/j.cosrev.2023.100546","volume":"48","author":"H Murtaza","year":"2023","unstructured":"Murtaza, H., Ahmed, M., Khan, N.F., Murtaza, G., Zafar, S., Bano, A.: Synthetic data generation: state of the art in health care domain. Comput. Sci. Rev. 48, 100546 (2023). https:\/\/doi.org\/10.1016\/j.cosrev.2023.100546","journal-title":"Comput. Sci. Rev."},{"key":"2_CR17","doi-asserted-by":"publisher","unstructured":"Nelsen, R.B.: An Introduction to Copulas. Springer, New York (2006). https:\/\/doi.org\/10.1007\/0-387-28678-0","DOI":"10.1007\/0-387-28678-0"},{"key":"2_CR18","unstructured":"Nielsen, D.: Tree boosting with XGBoost-why does XGBoost win \u201cevery\u201d machine learning competition? Master\u2019s thesis, NTNU (2016)"},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Oganian, A.: v-dispersed synthetic data based on a mixture model with constraints. In: Domingo-Ferrer, J. (eds.) Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, pp. 200\u2013212. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11257-2_16","DOI":"10.1007\/978-3-319-11257-2_16"},{"issue":"3","key":"2_CR20","first-page":"253","volume":"7","author":"Y Park","year":"2014","unstructured":"Park, Y., Ghosh, J.: PeGS: perturbed Gibbs samplers that generate privacy-compliant synthetic data. Trans. Data Priv. 7(3), 253\u2013282 (2014)","journal-title":"Trans. Data Priv."},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Park, Y., Ghosh, J., Shankar, M.: Perturbed Gibbs samplers for generating large-scale privacy-safe synthetic health data. In: 2013 IEEE International Conference on Healthcare Informatics. pp. 493\u2013498. IEEE (2013)","DOI":"10.1109\/ICHI.2013.76"},{"key":"2_CR22","doi-asserted-by":"publisher","unstructured":"Patki, N., Wedge, R., Veeramachaneni, K.: The synthetic data vault. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (2016). https:\/\/doi.org\/10.1109\/DSAA.2016.49","DOI":"10.1109\/DSAA.2016.49"},{"key":"2_CR23","doi-asserted-by":"publisher","unstructured":"Patki, N., Wedge, R., Veeramachaneni, K.: The synthetic data vault. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 399\u2013410 (2016). https:\/\/doi.org\/10.1109\/DSAA.2016.49","DOI":"10.1109\/DSAA.2016.49"},{"key":"2_CR24","doi-asserted-by":"publisher","unstructured":"Patki, N., Wedge, R., Veeramachaneni, K.: The synthetic data vault. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA), October 2016, pp. 399\u2013410 (2016). https:\/\/doi.org\/10.1109\/DSAA.2016.49","DOI":"10.1109\/DSAA.2016.49"},{"key":"2_CR25","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Restrepo, J.P., Rivera, J.C., Laniado, H., Osorio, P., Becerra, O.A.: Nonparametric generation of synthetic data using copulas. Electronics 12(7) (2023)","DOI":"10.3390\/electronics12071601"},{"issue":"2","key":"2_CR27","first-page":"461","volume":"9","author":"DB Rubin","year":"1993","unstructured":"Rubin, D.B.: Statistical disclosure limitation. J. Official Stat. 9(2), 461\u2013468 (1993)","journal-title":"J. Official Stat."},{"key":"2_CR28","unstructured":"scikit-learn: QuantileTransformer (2024). https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.QuantileTransformer.html"},{"key":"2_CR29","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning, pp. 2256\u20132265. PMLR (2015)"},{"key":"2_CR30","doi-asserted-by":"publisher","unstructured":"Faria, T.R., et\u00a0al: Improving the mapping of leisure-time physical activity inequities: the use of artificial intelligence to advance estimates of small-areas in Brazil. Pub. Health 243, 105727 (2025). https:\/\/doi.org\/10.1016\/j.puhe.2025.105727","DOI":"10.1016\/j.puhe.2025.105727"},{"key":"2_CR31","unstructured":"Truda, G.: Generating tabular datasets under differential privacy. arXiv preprint arXiv:2308.14784 (2023)"},{"issue":"2","key":"2_CR32","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1111\/coin.12427","volume":"37","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Myles, P., Tucker, A.: Generating and evaluating cross-sectional synthetic electronic healthcare data: Preserving data utility and patient privacy. Comput. Intell. 37(2), 819\u2013851 (2021)","journal-title":"Comput. Intell."},{"key":"2_CR33","unstructured":"Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional GAN. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"2_CR34","unstructured":"Xu, L., Veeramachaneni, K.: Synthesizing tabular data using generative adversarial networks. arXiv preprint arXiv:1811.11264 (2018)"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: sequence generative adversarial nets with policy gradient. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a031 (2017)","DOI":"10.1609\/aaai.v31i1.10804"},{"issue":"4","key":"2_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3134428","volume":"42","author":"J Zhang","year":"2017","unstructured":"Zhang, J., Cormode, G., Procopiuc, C.M., Srivastava, D., Xiao, X.: PrivBayes: private data release via Bayesian networks. ACM TODS 42(4), 1\u201341 (2017)","journal-title":"ACM TODS"},{"key":"2_CR37","doi-asserted-by":"crossref","unstructured":"Zhou, Y., et al.: DiffLM: controllable synthetic data generation via diffusion language models. arXiv preprint arXiv:2411.03250 (2024)","DOI":"10.18653\/v1\/2025.findings-acl.1061"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15990-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T19:59:30Z","timestamp":1769716770000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15990-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032159892","9783032159908"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15990-8_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fortaleza-CE","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bracis.sbc.org.br\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}