{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T10:59:28Z","timestamp":1756810768553,"version":"3.40.3"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031139444"},{"type":"electronic","value":"9783031139451"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-13945-1_17","type":"book-chapter","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T23:02:54Z","timestamp":1663110174000},"page":"234-249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Comparing the\u00a0Utility and\u00a0Disclosure Risk of\u00a0Synthetic Data with\u00a0Samples of\u00a0Microdata"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4803-3007","authenticated-orcid":false,"given":"Claire","family":"Little","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3142-4493","authenticated-orcid":false,"given":"Mark","family":"Elliot","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1236-3143","authenticated-orcid":false,"given":"Richard","family":"Allmendinger","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,14]]},"reference":[{"key":"17_CR1","unstructured":"Benedetto, G., Stinson, M.H., Abowd, J.M.: The creation and use of the SIPP synthetic beta. Technical report, November, U.S. Census Bureau (2018)"},{"key":"17_CR2","doi-asserted-by":"publisher","unstructured":"Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and regression trees. Wadsworth International Group, Belmont, California (1984). https:\/\/doi.org\/10.1201\/9781315139470","DOI":"10.1201\/9781315139470"},{"issue":"1","key":"17_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"issue":"1","key":"17_CR4","first-page":"27","volume":"3","author":"G Caiola","year":"2010","unstructured":"Caiola, G., Reiter, J.P.: Random forests for generating partially synthetic, categorical data. Trans. Data Privacy 3(1), 27\u201342 (2010)","journal-title":"Trans. Data Privacy"},{"key":"17_CR5","unstructured":"Camino, R., Hammerschmidt, C., State, R.: Generating multi-categorical samples with generative adversarial networks. arXiv preprint arXiv:1807.01202 (2018)"},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Chen, H., Jajodia, S., Liu, J., Park, N., Sokolov, V., Subrahmanian, V.S.: Fake tables: using GANs to generate functional dependency preserving tables with bounded real data. In: IJCAI, pp. 2074\u20132080 (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/287","DOI":"10.24963\/ijcai.2019\/287"},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"11147","DOI":"10.1109\/ACCESS.2022.3144765","volume":"10","author":"FK Dankar","year":"2022","unstructured":"Dankar, F.K., Ibrahim, M.K., Ismail, L.: A multi-dimensional evaluation of synthetic data generators. IEEE Access 10, 11147\u201311158 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3144765","journal-title":"IEEE Access"},{"issue":"492","key":"17_CR8","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1198\/jasa.2010.ap09480","volume":"105","author":"J Drechsler","year":"2010","unstructured":"Drechsler, J., Reiter, J.P.: Sampling with synthesis: a new approach for releasing public use census microdata. J. Am. Stat. Assoc. 105(492), 1347\u20131357 (2010). https:\/\/doi.org\/10.1198\/jasa.2010.ap09480","journal-title":"J. Am. Stat. Assoc."},{"issue":"12","key":"17_CR9","doi-asserted-by":"publisher","first-page":"3232","DOI":"10.1016\/j.csda.2011.06.006","volume":"55","author":"J Drechsler","year":"2011","unstructured":"Drechsler, J., Reiter, J.P.: Sampling with synthesis: a new approach for releasing public use census microdata. Comput. Stat. Data Anal. 55(12), 3232\u20133243 (2011). https:\/\/doi.org\/10.1016\/j.csda.2011.06.006","journal-title":"Comput. Stat. Data Anal."},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Duncan, G.T., Keller-McNulty, S.A., Stokes, S.L.: Database security and confidentiality: examining disclosure risk vs. data utility through the R-U confidentiality map. Technical report, National Institute of Statistical Sciences (2004)","DOI":"10.1080\/09332480.2004.10554908"},{"key":"17_CR11","unstructured":"Elliot, M.: Final report on the disclosure risk associated with the synthetic data produced by the SYLLS team. Technical report, University of Manchester (2014). http:\/\/hummedia.manchester.ac.uk\/institutes\/cmist\/archive-publications\/reports\/2015-02%20-Report%20on%20disclosure%20risk%20analysis%20of%20synthpop%20synthetic%20versions%20of%20LCF_%20final.pdf"},{"key":"17_CR12","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc. (2014). https:\/\/proceedings.neurips.cc\/paper\/2014\/file\/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf"},{"key":"17_CR13","doi-asserted-by":"publisher","unstructured":"Hittmeir, M., Ekelhart, A., Mayer, R.: Utility and privacy assessments of synthetic data for regression tasks. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 5763\u20135772 (2019). https:\/\/doi.org\/10.1109\/BigData47090.2019.9005476","DOI":"10.1109\/BigData47090.2019.9005476"},{"key":"17_CR14","doi-asserted-by":"publisher","unstructured":"Hundepool, A., et al.: Statistical Disclosure Control. Wiley Series in Survey Methodology. Wiley, Hoboken (2012). https:\/\/doi.org\/10.1002\/9781118348239","DOI":"10.1002\/9781118348239"},{"key":"17_CR15","unstructured":"Joshi, C.: Generative adversarial networks (GANs) for synthetic dataset generation with binary classes (2019). https:\/\/datasciencecampus.ons.gov.uk\/projects\/generative-adversarial-networks-gans-for-synthetic-dataset-generation-with-binary-classes\/"},{"issue":"6","key":"17_CR16","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1111\/1740-9713.01466","volume":"17","author":"I Kaloskampis","year":"2020","unstructured":"Kaloskampis, I., Joshi, C., Cheung, C., Pugh, D., Nolan, L.: Synthetic data in the civil service. Significance 17(6), 18\u201323 (2020). https:\/\/doi.org\/10.1111\/1740-9713.01466","journal-title":"Significance"},{"issue":"3","key":"17_CR17","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1198\/000313006X124640","volume":"60","author":"AF Karr","year":"2006","unstructured":"Karr, A.F., Kohnen, C.N., Oganian, A., Reiter, J.P., Sanil, A.P.: A framework for evaluating the utility of data altered to protect confidentiality. Am. Stat. 60(3), 224\u2013232 (2006). https:\/\/doi.org\/10.1198\/000313006X124640","journal-title":"Am. Stat."},{"issue":"3","key":"17_CR18","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1111\/j.1751-5823.2011.00153.x","volume":"79","author":"SK Kinney","year":"2011","unstructured":"Kinney, S.K., Reiter, J.P., Reznek, A.P., Miranda, J., Jarmin, R.S., Abowd, J.M.: Towards unrestricted public use business microdata: the synthetic longitudinal business database. Int. Stat. Rev. 79(3), 362\u2013384 (2011). https:\/\/doi.org\/10.1111\/j.1751-5823.2011.00153.x","journal-title":"Int. Stat. Rev."},{"issue":"7553","key":"17_CR19","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015). https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"17_CR20","unstructured":"Little, C., Elliot, M., Allmendinger, R., Samani, S.S.: Generative adversarial networks for synthetic data generation: a comparative study. In: Joint UNECE\/Eurostat Expert Meeting on Statistical Data Confidentiality (2021). https:\/\/unece.org\/sites\/default\/files\/2021-12\/SDC2021_Day2_Little_AD.pdf"},{"issue":"2","key":"17_CR21","first-page":"407","volume":"9","author":"RJA Little","year":"1993","unstructured":"Little, R.J.A.: Statistical analysis of masked data. J. Off. Stat. 9(2), 407\u2013426 (1993)","journal-title":"J. Off. Stat."},{"key":"17_CR22","doi-asserted-by":"publisher","unstructured":"Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., Vilhuber, L.: Privacy: theory meets practice on the map. In: 2008 IEEE 24th International Conference on Data Engineering, pp. 277\u2013286 (2008). https:\/\/doi.org\/10.1109\/ICDE.2008.4497436","DOI":"10.1109\/ICDE.2008.4497436"},{"key":"17_CR23","doi-asserted-by":"publisher","unstructured":"Minnesota Population Center. Integrated Public Use Microdata Series, International: Version 7.3 [dataset]. IPUMS: IPUMs Census Data, Minneapolis (2020). https:\/\/doi.org\/10.18128\/D020.V7.2","DOI":"10.18128\/D020.V7.2"},{"key":"17_CR24","doi-asserted-by":"publisher","unstructured":"Nixon, M.P., Barrientos, A.F., Reiter, J.P., Slavkovi\u0107, A.: A latent class modeling approach for generating synthetic data and making posterior inferences from differentially private counts (2022). https:\/\/doi.org\/10.48550\/ARXIV.2201.10545","DOI":"10.48550\/ARXIV.2201.10545"},{"issue":"11","key":"17_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v074.i11","volume":"74","author":"B Nowok","year":"2016","unstructured":"Nowok, B., Raab, G.M., Dibben, C.: Synthpop: bespoke creation of synthetic data in R. J. Stat. Softw. 74(11), 1\u201326 (2016). https:\/\/doi.org\/10.18637\/jss.v074.i11","journal-title":"J. Stat. Softw."},{"issue":"3","key":"17_CR26","doi-asserted-by":"publisher","first-page":"785","DOI":"10.3233\/SJI-150153","volume":"33","author":"B Nowok","year":"2017","unstructured":"Nowok, B., Raab, G.M., Dibben, C.: Providing bespoke synthetic data for the UK longitudinal studies and other sensitive data with the synthpop package for R. Stat. J. IAOS 33(3), 785\u2013796 (2017). https:\/\/doi.org\/10.3233\/SJI-150153","journal-title":"Stat. J. IAOS"},{"key":"17_CR27","doi-asserted-by":"publisher","unstructured":"Office for National Statistics, Census Division, University of Manchester, Cathie Marsh Centre for Census and Survey Research: Census 1991: Individual Sample of Anonymised Records for Great Britain (SARs) (2013). https:\/\/doi.org\/10.5255\/UKDA-SN-7210-1","DOI":"10.5255\/UKDA-SN-7210-1"},{"issue":"10","key":"17_CR28","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.14778\/3231751.3231757","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. Proc. VLDB Endow. 11(10), 1071\u20131083 (2018). https:\/\/doi.org\/10.14778\/3231751.3231757","journal-title":"Proc. VLDB Endow."},{"key":"17_CR29","doi-asserted-by":"publisher","unstructured":"Ping, H., Stoyanovich, J., Howe, B.: DataSynthesizer: privacy-preserving synthetic datasets. In: Proceedings of the 29th International Conference on Scientific and Statistical Database Management. ACM, New York (2017). https:\/\/doi.org\/10.1145\/3085504.3091117","DOI":"10.1145\/3085504.3091117"},{"key":"17_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-319-99771-1_7","volume-title":"Privacy in Statistical Databases","author":"M Pistner","year":"2018","unstructured":"Pistner, M., Slavkovi\u0107, A., Vilhuber, L.: Synthetic data via quantile regression for heavy-tailed and heteroskedastic data. In: Domingo-Ferrer, J., Montes, F. (eds.) PSD 2018. LNCS, vol. 11126, pp. 92\u2013108. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99771-1_7"},{"key":"17_CR31","doi-asserted-by":"publisher","unstructured":"Raab, G.M., Nowok, B., Dibben, C.: Guidelines for producing useful synthetic data (2017). https:\/\/doi.org\/10.48550\/ARXIV.1712.04078","DOI":"10.48550\/ARXIV.1712.04078"},{"issue":"1","key":"17_CR32","first-page":"1","volume":"19","author":"TE Raghunathan","year":"2003","unstructured":"Raghunathan, T.E., Reiter, J.P., Rubin, D.B.: Multiple imputation for statistical disclosure limitation. J. Off. Stat. 19(1), 1\u201316 (2003)","journal-title":"J. Off. Stat."},{"issue":"7","key":"17_CR33","doi-asserted-by":"publisher","first-page":"e18910","DOI":"10.2196\/18910","volume":"8","author":"D Rankin","year":"2020","unstructured":"Rankin, D., Black, M., Bond, R., Wallace, J., Mulvenna, M., Epelde, G.: Reliability of supervised machine learning using synthetic data in health care: model to preserve privacy for data sharing. JMIR Med. Inform. 8(7), e18910 (2020). https:\/\/doi.org\/10.2196\/18910","journal-title":"JMIR Med. Inform."},{"issue":"3","key":"17_CR34","first-page":"441","volume":"21","author":"J Reiter","year":"2005","unstructured":"Reiter, J.: Using CART to generate partially synthetic public use microdata. J. Off. Stat. 21(3), 441\u2013462 (2005)","journal-title":"J. Off. Stat."},{"issue":"4","key":"17_CR35","first-page":"531","volume":"18","author":"JP Reiter","year":"2002","unstructured":"Reiter, J.P.: Satisfying disclosure restrictions with synthetic data sets. J. Off. Stat. 18(4), 531 (2002)","journal-title":"J. Off. Stat."},{"issue":"2","key":"17_CR36","first-page":"181","volume":"29","author":"JP Reiter","year":"2003","unstructured":"Reiter, J.P.: Inference for partially synthetic, public use microdata sets. Surv. Methodol. 29(2), 181\u2013188 (2003)","journal-title":"Surv. Methodol."},{"issue":"1","key":"17_CR37","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1111\/j.1467-985X.2004.00343.x","volume":"168","author":"JP Reiter","year":"2003","unstructured":"Reiter, J.P.: Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study. J. R. Stat. Soc. Ser. A Stat. Soc. 168(1), 185\u2013205 (2003). https:\/\/doi.org\/10.1111\/j.1467-985X.2004.00343.x","journal-title":"J. R. Stat. Soc. Ser. A Stat. Soc."},{"issue":"2","key":"17_CR38","first-page":"461","volume":"9","author":"DB Rubin","year":"1993","unstructured":"Rubin, D.B.: Statistical disclosure limitation. J. Off. Stat. 9(2), 461\u2013468 (1993)","journal-title":"J. Off. Stat."},{"issue":"3","key":"17_CR39","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1111\/rssa.12358","volume":"181","author":"J Snoke","year":"2018","unstructured":"Snoke, J., Raab, G.M., Nowok, B., Dibben, C., Slavkovic, A.: General and specific utility measures for synthetic data. J. Roy. Stat. Soc. Ser. A Stat. Soc. 181(3), 663\u2013688 (2018). https:\/\/doi.org\/10.1111\/rssa.12358","journal-title":"J. Roy. Stat. Soc. Ser. A Stat. Soc."},{"key":"17_CR40","unstructured":"Taub, J., Elliot, M.: The synthetic data challenge. Joint UNECE\/Eurostat Work Session on Statistical Data Confidentiality (2019). https:\/\/unece.org\/fileadmin\/DAM\/stats\/documents\/ece\/ces\/ge.46\/2019\/mtg1\/SDC2019_S3_UK_Synthethic_Data_Challenge_Elliot_AD.pdf"},{"key":"17_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-319-99771-1_9","volume-title":"Privacy in Statistical Databases","author":"J Taub","year":"2018","unstructured":"Taub, J., Elliot, M., Pampaka, M., Smith, D.: Differential correct attribution probability for synthetic data: an exploration. In: Domingo-Ferrer, J., Montes, F. (eds.) PSD 2018. LNCS, vol. 11126, pp. 122\u2013137. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99771-1_9"},{"issue":"1","key":"17_CR42","first-page":"1","volume":"13","author":"J Taub","year":"2020","unstructured":"Taub, J., Elliot, M., Sakshaug, J.W.: The impact of synthetic data generation on data utility with application to the 1991 UK samples of anonymised records. Trans. Data Priv. 13(1), 1\u201323 (2020)","journal-title":"Trans. Data Priv."},{"key":"17_CR43","unstructured":"Therneau, T., Atkinson, E., Ripley, B.: Package \u2018rpart\u2019 (2019). https:\/\/cran.r-project.org\/package=rpart"},{"key":"17_CR44","doi-asserted-by":"publisher","first-page":"63514","DOI":"10.1109\/ACCESS.2020.2982224","volume":"8","author":"L Wang","year":"2020","unstructured":"Wang, L., Chen, W., Yang, W., Bi, F., Yu, F.R.: A state-of-the-art review on image synthesis with generative adversarial networks. IEEE Access 8, 63514\u201363537 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2982224","journal-title":"IEEE Access"},{"key":"17_CR45","unstructured":"Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional GAN. In: Advances in Neural Information Processing Systems, Vancouver, Canada, vol. 32 (2019). https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/254ed7d2de3b23ab10936522dd547b78-Paper.pdf"},{"issue":"4","key":"17_CR46","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). https:\/\/doi.org\/10.1145\/3134428","journal-title":"ACM Trans. Database Syst."},{"key":"17_CR47","unstructured":"Zhao, Z., Kunar, A., Birke, R., Chen, L.Y.: CTAB-GAN: effective table data synthesizing. In: Proceedings of 13th Asian Conference on Machine Learning, vol. 157, pp. 97\u2013112. PMLR (2021). https:\/\/proceedings.mlr.press\/v157\/zhao21a.html"}],"container-title":["Lecture Notes in Computer Science","Privacy in Statistical Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13945-1_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:37:57Z","timestamp":1710329877000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13945-1_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031139444","9783031139451"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13945-1_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PSD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Privacy in Statistical Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Paris","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"psd2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/crises-deim.urv.cat\/psd2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}