{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:51:50Z","timestamp":1783702310007,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Guangdong-Macao Advanced Intelligent Computing Joint Laboratory","award":["2020B1212030003"],"award-info":[{"award-number":["2020B1212030003"]}]},{"name":"National Key R&D Program of China","award":["2022YFC3303603"],"award-info":[{"award-number":["2022YFC3303603"]}]},{"name":"NSFC","award":["62077028, 62377028"],"award-info":[{"award-number":["62077028, 62377028"]}]},{"name":"Key Laboratory of Smart Education of Guangdong Higher Education Institutes, Jinan University","award":["2022LSYS003"],"award-info":[{"award-number":["2022LSYS003"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589335.3651511","type":"proceedings-article","created":{"date-parts":[[2024,5,12]],"date-time":"2024-05-12T18:41:21Z","timestamp":1715539281000},"page":"806-809","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Generating Privacy-preserving Educational Data Records with Diffusion Model"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6911-3853","authenticated-orcid":false,"given":"Quanlong","family":"Guan","sequence":"first","affiliation":[{"name":"Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9124-4386","authenticated-orcid":false,"given":"Yanchong","family":"Yu","sequence":"additional","affiliation":[{"name":"Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3473-9810","authenticated-orcid":false,"given":"Xiujie","family":"Huang","sequence":"additional","affiliation":[{"name":"Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6435-6570","authenticated-orcid":false,"given":"Liangda","family":"Fang","sequence":"additional","affiliation":[{"name":"Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6651-1175","authenticated-orcid":false,"given":"Chaobo","family":"He","sequence":"additional","affiliation":[{"name":"South China Normal University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4996-3356","authenticated-orcid":false,"given":"Lusheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5605-7397","authenticated-orcid":false,"given":"Weiqi","family":"Luo","sequence":"additional","affiliation":[{"name":"Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8236-3133","authenticated-orcid":false,"given":"Guanliang","family":"Chen","sequence":"additional","affiliation":[{"name":"Monash University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Martin Abadi Andy Chu Ian Goodfellow H Brendan McMahan Ilya Mironov Kunal Talwar and Li Zhang. 2016. Deep learning with differential privacy. In CCS. 308--318.","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3569576"},{"key":"e_1_3_2_2_3_1","first-page":"1","article-title":"Privacy and synthetic datasets","volume":"22","author":"Bellovin Steven M","year":"2019","unstructured":"Steven M Bellovin, Preetam K Dutta, and Nathan Reitinger. 2019. Privacy and synthetic datasets. Stan. Tech. L. Rev. , Vol. 22 (2019), 1.","journal-title":"Stan. Tech. L. Rev."},{"key":"e_1_3_2_2_4_1","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume":"34","author":"Dhariwal Prafulla","year":"2021","unstructured":"Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. NeurIPS , Vol. 34 (2021), 8780--8794.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_5_1","volume-title":"Differential privacy","author":"Dwork Cynthia","unstructured":"Cynthia Dwork. 2006. Differential privacy. In ICALP. Springer, 1--12."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_3_2_2_7_1","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. NeurIPS , Vol. 33, 6840--6851.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_8_1","first-page":"12454","article-title":"Argmax flows and multinomial diffusion: Learning categorical distributions","volume":"34","author":"Hoogeboom Emiel","year":"2021","unstructured":"Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forr\u00e9, and Max Welling. 2021. Argmax flows and multinomial diffusion: Learning categorical distributions. Advances in Neural Information Processing Systems , Vol. 34 (2021), 12454--12465.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.03.014"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITHET56107.2022.10031904"},{"key":"e_1_3_2_2_11_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2876034.2893404"},{"key":"e_1_3_2_2_13_1","volume-title":"Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456","author":"Song Yang","year":"2020","unstructured":"Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2020. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)."},{"key":"e_1_3_2_2_14_1","unstructured":"Theresa Stadler Bristena Oprisanu and Carmela Troncoso. 2022. Synthetic data - anonymisation groundhog day. In USENIX Security. 1451--1468."},{"key":"e_1_3_2_2_15_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_3_2_2_16_1","volume-title":"NeurIPS","volume":"32","author":"Xu Lei","year":"2019","unstructured":"Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019. Modeling tabular data using conditional gan. NeurIPS , Vol. 32 (2019)."},{"key":"e_1_3_2_2_17_1","volume-title":"Ctab-gan: Enhancing tabular data synthesis. arXiv preprint arXiv:2204.00401","author":"Zhao Zilong","year":"2022","unstructured":"Zilong Zhao, Aditya Kunar, Robert Birke, and Lydia Y Chen. 2022. Ctab-gan: Enhancing tabular data synthesis. arXiv preprint arXiv:2204.00401 (2022). io"}],"event":{"name":"WWW '24: The ACM Web Conference 2024","location":"Singapore Singapore","acronym":"WWW '24","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Companion Proceedings of the ACM Web Conference 2024"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589335.3651511","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589335.3651511","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:35:39Z","timestamp":1755822939000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589335.3651511"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":17,"alternative-id":["10.1145\/3589335.3651511","10.1145\/3589335"],"URL":"https:\/\/doi.org\/10.1145\/3589335.3651511","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}