{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T15:23:51Z","timestamp":1771946631619,"version":"3.50.1"},"reference-count":83,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T00:00:00Z","timestamp":1692057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T00:00:00Z","timestamp":1692057600000},"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":["The VLDB Journal"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00778-023-00807-y","type":"journal-article","created":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T09:02:06Z","timestamp":1692090126000},"page":"255-280","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Tabular data synthesis with generative adversarial networks: design space and optimizations"],"prefix":"10.1007","volume":"33","author":[{"given":"Tongyu","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4729-9903","authenticated-orcid":false,"given":"Ju","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Nan","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Xiaoyong","family":"Du","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,15]]},"reference":[{"key":"807_CR1","unstructured":"Adult data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Adult"},{"key":"807_CR2","unstructured":"Anuran calls (mfccs) data set. http:\/\/archive.ics.uci.edu\/ml\/datasets\/Anuran+Calls+%28MFCCs%29"},{"key":"807_CR3","doi-asserted-by":"crossref","unstructured":"Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: PODS (2001)","DOI":"10.1145\/375551.375602"},{"key":"807_CR4","unstructured":"Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, Conference Track Proceedings. OpenReview.net (2017)"},{"key":"807_CR5","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. CoRR arXiv:1701.07875 (2017)"},{"issue":"3","key":"807_CR6","first-page":"228","volume":"26","author":"MK Baowaly","year":"2019","unstructured":"Baowaly, M.K., Lin, C., Liu, C., Chen, K.: Synthesizing electronic health records using improved generative adversarial networks. JAMIA 26(3), 228\u2013241 (2019)","journal-title":"JAMIA"},{"key":"807_CR7","doi-asserted-by":"crossref","unstructured":"Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., Talwar, K.: Privacy, accuracy, and consistency too: a holistic solution to contingency table release. In: PODS, pp. 273\u2013282 (2007)","DOI":"10.1145\/1265530.1265569"},{"key":"807_CR8","doi-asserted-by":"crossref","unstructured":"Bohannon, P., Fan, W., Geerts, F., Jia, X., Kementsietsidis, A.: Conditional functional dependencies for data cleaning. In: ICDE, pp. 746\u2013755 (2007)","DOI":"10.1109\/ICDE.2007.367920"},{"key":"807_CR9","unstructured":"Borisov, V., Leemann, T., Se\u00dfler, K., Haug, J., Pawelczyk, M., Kasneci, G.: Deep neural networks and tabular data: a survey. CoRR arXiv:2110.01889 (2021)"},{"key":"807_CR10","doi-asserted-by":"crossref","unstructured":"Brickell, J., Shmatikov, V.: The cost of privacy: destruction of data-mining utility in anonymized data publishing. In: SIGKDD, pp. 70\u201378 (2008)","DOI":"10.1145\/1401890.1401904"},{"key":"807_CR11","unstructured":"Census-income (kdd) data set. http:\/\/archive.ics.uci.edu\/ml\/datasets\/Census-Income+(KDD)"},{"key":"807_CR12","doi-asserted-by":"crossref","unstructured":"Chaudhuri, S., Ding, B., Kandula, S.: Approximate query processing: No silver bullet. In: SIGMOD, pp. 511\u2013519 (2017)","DOI":"10.1145\/3035918.3056097"},{"key":"807_CR13","doi-asserted-by":"crossref","unstructured":"Chen, H., Jajodia, S., Liu, J., Park, N., Sokolov, V., Subrahmanian, V.S.: Faketables: using GANs to generate functional dependency preserving tables with bounded real data. In: IJCAI, pp. 2074\u20132080 (2019)","DOI":"10.24963\/ijcai.2019\/287"},{"key":"807_CR14","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: NIPS, pp. 2172\u20132180 (2016)"},{"key":"807_CR15","unstructured":"Choi, E., Biswal, S., Malin, B.A., Duke, J., Stewart, W.F., Sun, J.: Generating multi-label discrete electronic health records using generative adversarial networks. CoRR arXiv:1703.06490 (2017)"},{"key":"807_CR16","unstructured":"Covertype data set. http:\/\/archive.ics.uci.edu\/ml\/datasets\/covertype"},{"issue":"1\u20133","key":"807_CR17","first-page":"1","volume":"4","author":"G Cormode","year":"2012","unstructured":"Cormode, G., Garofalakis, M.N., Haas, P.J., Jermaine, C.: Synopses for massive data: samples, histograms, wavelets, sketches. Found. Trends Databases 4(1\u20133), 1\u2013294 (2012)","journal-title":"Found. Trends Databases"},{"key":"807_CR18","unstructured":"Doersch, C.: Tutorial on variational autoencoders. CoRR arXiv:1606.05908 (2016)"},{"key":"807_CR19","unstructured":"Diabete data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Diabetes+130-US+hospitals+for+years+1999-2008"},{"key":"807_CR20","doi-asserted-by":"crossref","unstructured":"Domingo-Ferrer, J.: A survey of inference control methods for privacy-preserving data mining. In: Privacy-Preserving Data Mining\u2014Models and Algorithms, pp. 53\u201380 (2008)","DOI":"10.1007\/978-0-387-70992-5_3"},{"key":"807_CR21","unstructured":"Dumoulin, V., Belghazi, I., Poole, B., Lamb, A., Arjovsky, M., Mastropietro, O., Courville, A.C.: Adversarially learned inference. In: ICLR (2017)"},{"issue":"3\u20134","key":"807_CR22","first-page":"211","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3\u20134), 211\u2013407 (2014)","journal-title":"Found. Trends Theor. Comput. Sci."},{"key":"807_CR23","unstructured":"Esteban, C., Hyland, S.L., R\u00e4tsch, G.: Real-valued (medical) time series generation with recurrent conditional GANs. CoRR arXiv:1706.02633 (2017)"},{"key":"807_CR24","doi-asserted-by":"crossref","unstructured":"Fan, J., Liu, T., Li, G., Chen, J., Shen, Y., Du, X.: Relation data synthesis using generative adversarial network: a design space exploration. In: Technical Report. https:\/\/github.com\/ruclty\/Daisy\/blob\/master\/daisy.pdf (2020)","DOI":"10.14778\/3407790.3407802"},{"issue":"11","key":"807_CR25","doi-asserted-by":"publisher","first-page":"1962","DOI":"10.14778\/3407790.3407802","volume":"13","author":"J Fan","year":"2020","unstructured":"Fan, J., Liu, T., Li, G., Chen, J., Shen, Y., Du, X.: Relational data synthesis using generative adversarial networks: a design space exploration. Proc. VLDB Endow. 13(11), 1962\u20131975 (2020)","journal-title":"Proc. VLDB Endow."},{"key":"807_CR26","doi-asserted-by":"crossref","unstructured":"Gondara, L., Wang, K.: MIDA: multiple imputation using denoising autoencoders. In: PAKDD, pp. 260\u2013272 (2018)","DOI":"10.1007\/978-3-319-93040-4_21"},{"key":"807_CR27","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: Generative adversarial nets. In: NIPS, pp. 2672\u20132680 (2014)"},{"key":"807_CR28","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition. Springer Series in Statistics. Springer (2009)","DOI":"10.1007\/978-0-387-84858-7"},{"issue":"8","key":"807_CR29","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"807_CR30","unstructured":"Htru2 data set. http:\/\/archive.ics.uci.edu\/ml\/datasets\/HTRU2"},{"key":"807_CR31","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML, pp. 448\u2013456 (2015)"},{"key":"807_CR32","unstructured":"Internet data set. https:\/\/openml.org\/search?type=data &status=active &id=372"},{"key":"807_CR33","unstructured":"Jordon, J., Yoon, J., van der Schaar, M.: PATE-GAN: generating synthetic data with differential privacy guarantees. In: ICLR (2019)"},{"key":"807_CR34","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1001\/jama.282.15.1466","volume":"282","author":"JG Hodge Jr","year":"1999","unstructured":"Hodge, J.G., Jr., Gostin, L.O., Jacobson, P.: Legal issues concerning electronic health information: privacy, quality, and liability. JAMA 282, 1466\u20131471 (1999)","journal-title":"JAMA"},{"key":"807_CR35","unstructured":"Kaggle. The state of data science and machine learning. https:\/\/www.kaggle.com\/surveys\/2017 (2017)"},{"key":"807_CR36","unstructured":"Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. In: Larochelle, H., Ranzato M., Hadsell R., Balcan M., Lin H. (eds) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, virtual (2020)"},{"issue":"1","key":"807_CR37","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s41019-022-00176-6","volume":"7","author":"S Khope","year":"2022","unstructured":"Khope, S., Elias, S.: Critical correlation of predictors for an efficient risk prediction framework of ICU patient using correlation and transformation of MIMIC-III dataset. Data Sci. Eng. 7(1), 71\u201386 (2022)","journal-title":"Data Sci. Eng."},{"key":"807_CR38","doi-asserted-by":"crossref","unstructured":"Kim, J., Jeon, J., Lee, J., Hyeong, J., Park, N.: OCT-GAN: neural ode-based conditional tabular GANs. In Leskovec J., Grobelnik M., Najork M., Tang J., Zia L. (eds) WWW \u201921: The Web Conference 2021, Virtual Event \/ Ljubljana, pp. 1506\u20131515. ACM\/IW3C2 (2021)","DOI":"10.1145\/3442381.3449999"},{"key":"807_CR39","unstructured":"Kim, J., Lee, C., Park, N.: Stasy: score-based tabular data synthesis. CoRR arXiv:2210.04018 (2022)"},{"key":"807_CR40","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, C., Shin, Y., Park, S., Kim, M., Park, N., Cho, J.: SOS: score-based oversampling for tabular data. In: Zhang A., Rangwala H. (eds) KDD \u201922: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, pp. 762\u2013772, ACM (2022)","DOI":"10.1145\/3534678.3539454"},{"key":"807_CR41","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: ICLR (2014)"},{"key":"807_CR42","unstructured":"Kotelnikov, A.,\u00a0Baranchuk, D.,\u00a0Rubachev, I., Babenko, A.: Tabddpm: Modelling tabular data with diffusion models. CoRR arXiv:2209.15421 (2022)"},{"key":"807_CR43","unstructured":"Lee, J., Hyeong, J., Jeon, J., Park, N., Cho, J.: Invertible tabular GANs: killing two birds with one stone for tabular data synthesis. In: Ranzato M., Beygelzimer A., Dauphin Y.N., Liang P., Vaughan J.W. (eds) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, pp. 4263\u20134273 (2021)"},{"issue":"13","key":"807_CR44","first-page":"1677","volume":"7","author":"H Li","year":"2014","unstructured":"Li, H., Xiong, L., Zhang, L., Jiang, X.: Dpsynthesizer: differentially private data synthesizer for privacy preserving data sharing. PVLDB 7(13), 1677\u20131680 (2014)","journal-title":"PVLDB"},{"issue":"12","key":"807_CR45","doi-asserted-by":"publisher","first-page":"2262","DOI":"10.1109\/TKDE.2018.2877362","volume":"31","author":"K Li","year":"2019","unstructured":"Li, K., Zhang, Y., Li, G., Tao, W., Yan, Y.: Bounded approximate query processing. IEEE Trans. Knowl. Data Eng. 31(12), 2262\u20132276 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"807_CR46","doi-asserted-by":"crossref","unstructured":"Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: ICDE, pp. 106\u2013115 (2007)","DOI":"10.1109\/ICDE.2007.367856"},{"key":"807_CR47","unstructured":"Li, S.C., Jiang, B., Marlin, B.M.: Misgan: learning from incomplete data with generative adversarial networks. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, OpenReview.net (2019)"},{"issue":"13","key":"807_CR48","first-page":"1766","volume":"7","author":"ZJ Ling","year":"2014","unstructured":"Ling, Z.J., Tran, Q.T., Fan, J., Koh, G.C.H., Nguyen, T., Tan, C.S., Yip, J.W.L., Zhang, M.: GEMINI: an integrative healthcare analytics system. PVLDB 7(13), 1766\u20131771 (2014)","journal-title":"PVLDB"},{"issue":"7","key":"807_CR49","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.14778\/3450980.3450989","volume":"14","author":"T Liu","year":"2021","unstructured":"Liu, T., Fan, J., Luo, Y., Tang, N., Li, G., Du, X.: Adaptive data augmentation for supervised learning over missing data. Proc. VLDB Endow. 14(7), 1202\u20131214 (2021)","journal-title":"Proc. VLDB Endow."},{"key":"807_CR50","doi-asserted-by":"crossref","unstructured":"Liu, T., Yang, J., Fan, J., Wei, Z., Li, G., Du, X.: Crowdgame: a game-based crowdsourcing system for cost-effective data labeling. In: SIGMOD, pp. 1957\u20131960 (2019)","DOI":"10.1145\/3299869.3320221"},{"key":"807_CR51","doi-asserted-by":"crossref","unstructured":"Lu, P., Wang, P., Yu, C.: Empirical evaluation on synthetic data generation with generative adversarial network. In: WIMS, vol. 16, pp. 1\u201316 (2019)","DOI":"10.1145\/3326467.3326474"},{"key":"807_CR52","unstructured":"Lucic, M., Kurach, K., Michalski, M., Gelly, S., Bousquet, O.: Are GANs created equal? A large-scale study. In: NeurIPS, pp. 698\u2013707 (2018)"},{"key":"807_CR53","doi-asserted-by":"crossref","unstructured":"Mateo-Sanz, J.M., Seb\u00e9, F., Domingo-Ferrer, J.: Outlier protection in continuous microdata masking. In: Privacy in Statistical Databases, pp. 201\u2013215 (2004)","DOI":"10.1007\/978-3-540-25955-8_16"},{"key":"807_CR54","unstructured":"Metz, L., Poole, B., Pfau, D., Sohl-Dickstein, J.: Unrolled generative adversarial networks. CoRR arXiv:1611.02163 (2016)"},{"key":"807_CR55","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. CoRR arXiv:1411.1784 (2014)"},{"key":"807_CR56","unstructured":"Olsson, C., Bhupatiraju, S., Brown, T.B., Odena, A., Goodfellow, I.J.: Skill rating for generative models. CoRR arXiv:1808.04888"},{"issue":"10","key":"807_CR57","first-page":"1071","volume":"11","author":"N Park","year":"2018","unstructured":"Park, N., Mohammadi, M., Gorde, K., Jajodia, S., Park, H., Kim, Y.: Data synthesis based on generative adversarial networks. PVLDB 11(10), 1071\u20131083 (2018)","journal-title":"PVLDB"},{"issue":"3","key":"807_CR58","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 Privacy 7(3), 253\u2013282 (2014)","journal-title":"Trans. Data Privacy"},{"key":"807_CR59","doi-asserted-by":"crossref","unstructured":"Patki, N., Wedge, R., Veeramachaneni, K.: The synthetic data vault. In: DSAA, pp. 399\u2013410 (2016)","DOI":"10.1109\/DSAA.2016.49"},{"key":"807_CR60","unstructured":"Pen-based recognition of handwritten digits data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Pen-Based+Recognition+of+Handwritten+Digits"},{"key":"807_CR61","unstructured":"PyTorch Developers. Tensors and dynamic neural networks in python with strong GPU acceleration. https:\/\/pytorch.org"},{"key":"807_CR62","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR (2016)"},{"key":"807_CR63","unstructured":"Ramakrishnan, R., Gehrke, J.: Database Management Systems, 3rd edn. McGraw-Hill (2003)"},{"key":"807_CR64","unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: ICML, pp. 1278\u20131286 (2014)"},{"key":"807_CR65","unstructured":"Salimans, T., Goodfellow, I.J., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: NIPS, pp. 2226\u20132234 (2016)"},{"issue":"4","key":"807_CR66","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s41019-021-00167-z","volume":"6","author":"R Sarki","year":"2021","unstructured":"Sarki, R., Ahmed, K., Wang, H., et al.: Image preprocessing in classification and identification of diabetic eye diseases. Data Sci. Eng. 6(4), 455\u2013471 (2021)","journal-title":"Data Sci. Eng."},{"key":"807_CR67","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy, SP 2017, San Jose, pp. 3\u201318. IEEE Computer Society (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"807_CR68","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS, pp. 3104\u20133112 (2014)"},{"key":"807_CR69","unstructured":"Statlog (landsat satellite) data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Statlog+%28Landsat+Satellite%29"},{"key":"807_CR70","doi-asserted-by":"crossref","unstructured":"Thirumuruganathan, S., Hasan, S., Koudas, N., Das, G.: Approximate query processing using deep generative models. CoRR arXiv:1903.10000 (2019)","DOI":"10.1109\/ICDE48307.2020.00117"},{"issue":"8","key":"807_CR71","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/TKDE.2010.247","volume":"23","author":"X Xiao","year":"2011","unstructured":"Xiao, X., Wang, G., Gehrke, J.: Differential privacy via wavelet transforms. IEEE Trans. Knowl. Data Eng. 23(8), 1200\u20131214 (2011)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"807_CR72","unstructured":"Xie, L., Lin, K., Wang, S., Wang, F., Zhou, J.: Differentially private generative adversarial network. CoRR arXiv:1802.06739 (2018)"},{"key":"807_CR73","unstructured":"Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional GAN. CoRR arXiv:1907.00503 (2019)"},{"key":"807_CR74","unstructured":"Xu, L., Veeramachaneni, K.: Synthesizing tabular data using generative adversarial networks. CoRR arXiv:1811.11264 (2018)"},{"issue":"1","key":"807_CR75","first-page":"57","volume":"12","author":"J Yang","year":"2018","unstructured":"Yang, J., Fan, J., Wei, Z., Li, G., Liu, T., Du, X.: Cost-effective data annotation using game-based crowdsourcing. PVLDB 12(1), 57\u201370 (2018)","journal-title":"PVLDB"},{"key":"807_CR76","unstructured":"Yang, L., Chou, S., Yang, Y.: Midinet: a convolutional generative adversarial network for symbolic-domain music generation. In: ISMIR, pp. 324\u2013331 (2017)"},{"key":"807_CR77","unstructured":"Yao, Q., Wang, M., Chen, Y., Dai, W., Li, Y.-F., Tu, W.-W., Yang, Q., Yu, Y.: Taking human out of learning applications: a survey on automated machine learning. Preprint arXiv:1810.13306 (2018)"},{"key":"807_CR78","doi-asserted-by":"crossref","unstructured":"Yu, L., Zhang, W., Wang, J., Yu, Y.: Seqgan: sequence generative adversarial nets with policy gradient. In: AAAI, pp. 2852\u20132858 (2017)","DOI":"10.1609\/aaai.v31i1.10804"},{"key":"807_CR79","unstructured":"Zhang, D., Khoreva, A.: PA-GAN: improving GAN training by progressive augmentation. CoRR arXiv:1901.10422 (2019)"},{"key":"807_CR80","doi-asserted-by":"crossref","unstructured":"Zhang, J., Cormode, G., Procopiuc, C.M., Srivastava, D., Xiao, X.: Privbayes: private data release via Bayesian networks. In: SIGMOD, pp. 1423\u20131434 (2014)","DOI":"10.1145\/2588555.2588573"},{"issue":"4","key":"807_CR81","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 Trans. Database Syst. 42(4), 1\u201341 (2017)","journal-title":"ACM Trans. Database Syst."},{"issue":"1","key":"807_CR82","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1093\/jamia\/ocz161","volume":"27","author":"Z Zhang","year":"2020","unstructured":"Zhang, Z., Yan, C., Mesa, D.A., Sun, J., Malin, B.A.: Ensuring electronic medical record simulation through better training, modeling, and evaluation. J. Am. Med. Inform. Assoc. 27(1), 99\u2013108 (2020)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"807_CR83","unstructured":"Zhao, S., Liu, Z., Lin, J., Zhu, J., Han, S.: Differentiable augmentation for data-efficient GAN training. In: Larochelle H., Ranzato M., Hadsell R., Balcan M., Lin H. (eds) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020 (2020)"}],"container-title":["The VLDB Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-023-00807-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00778-023-00807-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-023-00807-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T05:26:19Z","timestamp":1729920379000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00778-023-00807-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,15]]},"references-count":83,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["807"],"URL":"https:\/\/doi.org\/10.1007\/s00778-023-00807-y","relation":{},"ISSN":["1066-8888","0949-877X"],"issn-type":[{"value":"1066-8888","type":"print"},{"value":"0949-877X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,15]]},"assertion":[{"value":"30 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}