{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T21:12:11Z","timestamp":1762809131329,"version":"3.40.3"},"publisher-location":"Cham","reference-count":70,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030766627"},{"type":"electronic","value":"9783030766634"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-76663-4_6","type":"book-chapter","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T08:03:46Z","timestamp":1621325026000},"page":"106-119","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Towards Improving Privacy of Synthetic DataSets"],"prefix":"10.1007","author":[{"given":"Aditya","family":"Kuppa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lamine","family":"Aouad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nhien-An","family":"Le-Khac","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"6_CR1","unstructured":"Carlini, N., et al.: Extracting training data from large language models. arXiv preprint arXiv:2012.07805 (2020)"},{"key":"6_CR2","unstructured":"SIVEP-Gripe (2020). http:\/\/plataforma.saude.gov.br\/coronavirus\/dados-abertos\/. In Ministry of Health. SIVEP-Gripe public dataset, (Accessed 10 May 2020; in Portuguese)"},{"key":"6_CR3","unstructured":"Jouppi, N.P., et al.: In-datacenter performance analysis of a tensor processing unit. In: Proceedings of the 44th Annual International Symposium on Computer Architecture, pp. 1\u201312 (2017)"},{"key":"6_CR4","unstructured":"Departement of Commerce, National Institute of Standards and Technology. Differential private synthetic data challenge (2019). https:\/\/www.challenge.gov\/challenge\/differential-privacy-synthetic-data-challenge\/. Accessed 19 Feb 2021"},{"key":"6_CR5","unstructured":"Olivier, T.T.: Anonymisation and synthetic data: towards trustworthy data (2019). https:\/\/theodi.org\/article\/anonymisation-and-synthetic-data-towards-trustworthy-data\/. Accessed 19 Feb 2021"},{"key":"6_CR6","unstructured":"The Open Data Institute. Diagnosing the NHS: SynAE. https:\/\/www.odileeds.org\/events\/synae\/. Accessed 19 Feb 2021"},{"key":"6_CR7","unstructured":"Hazy. https:\/\/hazy.com\/"},{"key":"6_CR8","unstructured":"AIreverie. https:\/\/aireverie.com\/"},{"key":"6_CR9","unstructured":"Statice. https:\/\/statice.ai\/"},{"key":"6_CR10","unstructured":"One-view. https:\/\/one-view.ai\/"},{"key":"6_CR11","unstructured":"Datagen. https:\/\/www.datagen.tech\/"},{"key":"6_CR12","unstructured":"Synthesize. https:\/\/synthezise.io\/"},{"key":"6_CR13","unstructured":"Cognata. https:\/\/www.cognata.com\/"},{"key":"6_CR14","unstructured":"Mostly-AI. https:\/\/mostly.ai\/"},{"issue":"2","key":"6_CR15","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":"2","key":"6_CR16","first-page":"407","volume":"9","author":"RJ Little","year":"1993","unstructured":"Little, R.J.: Statistical analysis of masked data. J. Off. Stat. 9(2), 407\u2013426 (1993)","journal-title":"J. Off. Stat."},{"key":"6_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/978-3-540-25955-8_22","volume-title":"Privacy in Statistical Databases","author":"JM Abowd","year":"2004","unstructured":"Abowd, J.M., Lane, J.: New approaches to confidentiality protection: synthetic data, remote access and research data centers. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 282\u2013289. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-25955-8_22. ISBN 978-3-540-22118-0"},{"key":"6_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/978-3-540-25955-8_23","volume-title":"Privacy in Statistical Databases","author":"JM Abowd","year":"2004","unstructured":"Abowd, J.M., Woodcock, S.D.: Multiply-imputing confidential characteristics and file links in longitudinal linked data. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 290\u2013297. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-25955-8_23. ISBN 3-540-22118-2"},{"issue":"480","key":"6_CR19","doi-asserted-by":"publisher","first-page":"1462","DOI":"10.1198\/016214507000000932","volume":"102","author":"JP Reiter","year":"2007","unstructured":"Reiter, J.P., Raghunathan, T.E.: The multiple adaptations of multiple imputation. J. Am. Stat. Assoc. 102(480), 1462\u20131471 (2007)","journal-title":"J. Am. Stat. Assoc."},{"issue":"492","key":"6_CR20","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)","journal-title":"J. Am. Stat. Assoc."},{"issue":"12","key":"6_CR21","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.: An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. Comput. Stat. Data Anal. 55(12), 3232\u20133243 (2011)","journal-title":"Comput. Stat. Data Anal."},{"issue":"3","key":"6_CR22","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)","journal-title":"Int. Stat. Rev."},{"issue":"3","key":"6_CR23","first-page":"441","volume":"21","author":"JP Reiter","year":"2005","unstructured":"Reiter, J.P.: Using cart to generate partially synthetic, public use microdata. J. Off. Stat. 21(3), 441\u2013462 (2005)","journal-title":"J. Off. Stat."},{"issue":"1","key":"6_CR24","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 Priv. 3(1), 27\u201342 (2010)","journal-title":"Trans. Data Priv."},{"key":"6_CR25","doi-asserted-by":"publisher","unstructured":"Drechsler, J.: Synthetic Datasets for Statistical Disclosure Control, vol. 53. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-1-4614-0326-5. ISBN 9788578110796","DOI":"10.1007\/978-1-4614-0326-5"},{"key":"6_CR26","unstructured":"Bowen, C.M., Liu, F.: Comparative study of differentially private data synthesis methods. Stat. Sci. (forthcoming)"},{"issue":"3","key":"6_CR27","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1111\/rssa.12352","volume":"181","author":"D Manrique-Vallier","year":"2018","unstructured":"Manrique-Vallier, D., Hu, J.: Bayesian non-parametric generation of fully synthetic multivariate categorical data in the presence of structural zeros. J. Roy. Stat. Soc. Ser. A: Stat. Soc. 181(3), 635\u2013647 (2018)","journal-title":"J. Roy. Stat. Soc. Ser. A: Stat. Soc."},{"key":"6_CR28","unstructured":"Snoke, J., Raab, G., Nowok, B., Dibben, C., Slavkovic, A.: General and specific utility measures for synthetic data (2016)"},{"issue":"1","key":"6_CR29","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 (2003)","journal-title":"J. Off. Stat."},{"issue":"2","key":"6_CR30","first-page":"3","volume":"2","author":"SK Kinney","year":"2010","unstructured":"Kinney, S.K., Reiter, J.P., Berger, J.O.: Model selection when multiple imputation is used to protect confidentiality in public use data. J. Priv. Confident. 2(2), 3\u201319 (2010)","journal-title":"J. Priv. Confident."},{"key":"6_CR31","unstructured":"Article 29 Data Protection Working Party - European Commission. Opinion 05\/2014 on anonymisation techniques (2014). https:\/\/ec.europa.eu\/justice\/article-29\/documentation\/opinion-recommendation\/files\/2014\/wp216_en.pdf"},{"key":"6_CR32","unstructured":"Elliot, M., Mackey, E., O\u2019Hara, K., Tudor, C.: The anonymisation decision-making framework. UKAN Manchester (2016)"},{"key":"6_CR33","first-page":"703","volume":"91","author":"IS Rubinstein","year":"2016","unstructured":"Rubinstein, I.S., Hartzog, W.: Anonymization and risk. Wash. L. Rev. 91, 703 (2016)","journal-title":"Wash. L. Rev."},{"issue":"2","key":"6_CR34","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.clsr.2018.02.001","volume":"34","author":"M Elliot","year":"2018","unstructured":"Elliot, M., et al.: Functional anonymisation: personal data and the data environment. Comput. Law Secur. Rev. 34(2), 204\u2013221 (2018)","journal-title":"Comput. Law Secur. Rev."},{"key":"6_CR35","doi-asserted-by":"crossref","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. IEEE (2016)","DOI":"10.1109\/DSAA.2016.49"},{"key":"6_CR36","unstructured":"Goodfellow, I.: NIPS 2016 tutorial: generative adversarial networks. arXiv preprint arXiv:1701.00160 (2016)"},{"key":"6_CR37","unstructured":"European Commission. Regulation (EU) 2016\/679: General Data Protection Regulation (GDPR) (2016)"},{"issue":"05","key":"6_CR38","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1142\/S0218488502001648","volume":"10","author":"L Sweeney","year":"2002","unstructured":"Sweeney, L.: k-anonymity: a model for protecting privacy. Internat. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(05), 557\u2013570 (2002)","journal-title":"Internat. J. Uncertain. Fuzziness Knowl.-Based Syst."},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: IEEE Symposium on Security and Privacy (S&P) (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"6_CR40","unstructured":"Yaghini, M., Kulynych, B., Troncoso, C.: Disparate vulnerability: on the unfairness of privacy attacks against machine learning. arXiv preprint arXiv:1906.00389 (2019)"},{"key":"6_CR41","doi-asserted-by":"crossref","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 (2017)","DOI":"10.1145\/3085504.3091117"},{"key":"6_CR42","doi-asserted-by":"crossref","unstructured":"Jayaraman, B., Wang, L., Evans, D., Gu, Q.: Revisiting membership inference under realistic assumptions. arXiv preprint arXiv:2005.10881 (2020)","DOI":"10.2478\/popets-2021-0031"},{"key":"6_CR43","doi-asserted-by":"crossref","unstructured":"Yeom, S., Giacomelli, I., Fredrikson, M., Jha, S.: Privacy risk in machine learning: analyzing the connection to overfitting. In: 2018 IEEE 31st Computer Security Foundations Symposium (CSF), pp. 268\u2013282. IEEE (2018)","DOI":"10.1109\/CSF.2018.00027"},{"key":"6_CR44","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, 1\u201341 (2017)","journal-title":"ACM Trans. Database Syst."},{"key":"6_CR45","unstructured":"Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional GAN. In: Advances in Neural Information Processing Systems (2019)"},{"key":"6_CR46","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1109\/JBHI.2020.2980262","volume":"24","author":"J Yoon","year":"2020","unstructured":"Yoon, J., Drumright, L.N., Van Der Schaar, M.: Anonymization through data synthesis using generative adversarial networks (ADS-GAN). IEEE J. Biomed. Health Inf. 24, 2378\u20132388 (2020)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"6_CR47","unstructured":"Adlam, B., Weill, C., Kapoor, A.: Investigating under and overfitting in wasserstein generative adversarial networks. arXiv preprint arXiv:1910.14137 (2019)"},{"key":"6_CR48","unstructured":"Meehan, C., Chaudhuri, K., Dasgupta, S.: A non-parametric test to detect data-copying in generative models. arXiv preprint arXiv:2004.05675 (2020)"},{"issue":"1","key":"6_CR49","first-page":"133","volume":"2019","author":"J Hayes","year":"2019","unstructured":"Hayes, J., Melis, L., Danezis, G., De Cristofaro, E.: LoGAN: membership inference attacks against generative models. Proc. Priv. Enhanc. Technol. 2019(1), 133\u2013152 (2019)","journal-title":"Proc. Priv. Enhanc. Technol."},{"key":"6_CR50","unstructured":"Sablayrolles, A., Douze, M., Ollivier, Y., Schmid, C., J\u00e9gou, H.: White-box vs black-box: bayes optimal strategies for membership inference. In: Proceedings of the 36th International Conference on Machine Learning (ICML), pp. 5558\u20135567 (2019)"},{"key":"6_CR51","unstructured":"Sablayrolles, A., Douze, M., Ollivier, Y., Schmid, C., J\u00e9gou, H.: White-box vs black-box: bayes optimal strategies for membership inference (2019)"},{"key":"6_CR52","unstructured":"Truex, S., Liu, L., Gursoy, M.E., Yu, L., Wei, W.: Towards demystifying membership inference attacks. ArXiv, vol. abs\/1807.09173 (2018)"},{"key":"6_CR53","doi-asserted-by":"crossref","unstructured":"Kuppa, A., Grzonkowski, S., Asghar, M.R., Le-Khac, N.-A.: Black box attacks on deep anomaly detectors. In: Proceedings of the 14th International Conference on Availability, Reliability and Security (2019)","DOI":"10.1145\/3339252.3339266"},{"key":"6_CR54","unstructured":"Yoon, J., Jordon, J., van der Schaar, M.: PATE-GAN: generating synthetic data with differential privacy guarantees. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=S1zk9iRqF7"},{"key":"6_CR55","unstructured":"Arpit, D., et al.: A closer look at memorization in deep networks. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70, ICML 2017, p. 233\u2013242. JMLR.org (2017)"},{"key":"6_CR56","unstructured":"Salimans, T., Goodfellow, I.J., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. CoRR abs\/1606.03498 (2016)"},{"key":"6_CR57","unstructured":"Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional GAN. In: NeurIPS (2019)"},{"key":"6_CR58","unstructured":"Eduardo, S., Naz\u00e1bal, A., Williams, C.K.I., Sutton, C.: Robust variational autoencoders for outlier detection and repair of mixed-type data. In: Proceedings of AISTATS (2020)"},{"key":"6_CR59","unstructured":"Camino, R., Hammerschmidt, C., State, R.: Generating multi-categorical samples with generative adversarial networks. arXiv preprint arXiv:1807.01202 (2018)"},{"key":"6_CR60","unstructured":"Meehan, C.R., Chaudhuri, K., Dasgupta, S.: A non-parametric test to detect data-copying in generative models. ArXiv, vol. abs\/2004.05675 (2020)"},{"key":"6_CR61","unstructured":"Izzo, Z., Smart, M.A., Chaudhuri, K., Zou, J.: Approximate data deletion from machine learning models: algorithms and evaluations. ArXiv, vol. abs\/2002.10077 (2020)"},{"key":"6_CR62","unstructured":"Song, C., Shmatikov, V.: Overlearning reveals sensitive attributes (2020)"},{"key":"6_CR63","doi-asserted-by":"crossref","unstructured":"Melis, L., Song, C., De Cristofaro, E., Shmatikov, V.: Exploiting unintended feature leakage in collaborative learning. In: IEEE Symposium on Security and Privacy (S&P), pp. 497\u2013512. IEEE (2019)","DOI":"10.1109\/SP.2019.00029"},{"key":"6_CR64","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: IEEE Symposium on Security and Privacy (S&P), pp. 3\u201318. IEEE (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"6_CR65","doi-asserted-by":"crossref","unstructured":"Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., Zhang, Y.: When machine unlearning jeopardizes privacy. CoRR abs\/2005.02205 (2020)","DOI":"10.1145\/3460120.3484756"},{"key":"6_CR66","unstructured":"Li, Z., Zhang, Y.: Label-leaks: membership inference attack with label. CoRR abs\/2007.15528 (2020)"},{"key":"6_CR67","unstructured":"Leino, K., Fredrikson, M.: Stolen memories: leveraging model memorization for calibrated white-box membership inference. In: USENIX Security Symposium (USENIX Security), pp. 1605\u20131622. USENIX (2020)"},{"key":"6_CR68","doi-asserted-by":"crossref","unstructured":"Chen, D., Yu, N., Zhang, Y., Fritz, M.: GAN-leaks: a taxonomy of membership inference attacks against generative models. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), p. 343\u2013362. ACM (2020)","DOI":"10.1145\/3372297.3417238"},{"key":"6_CR69","doi-asserted-by":"crossref","unstructured":"Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., Backes, M.: ML-leaks: model and data independent membership inference attacks and defenses on machine learning models. In: Network and Distributed System Security Symposium (NDSS). Internet Society (2019)","DOI":"10.14722\/ndss.2019.23119"},{"key":"6_CR70","doi-asserted-by":"crossref","unstructured":"Jia, J., Salem, A., Backes, M., Zhang, Y., Gong, N.Z.: MemGuard: defending against black-box membership inference attacks via adversarial examples. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 259\u2013274. ACM (2019)","DOI":"10.1145\/3319535.3363201"}],"container-title":["Lecture Notes in Computer Science","Privacy Technologies and Policy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-76663-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T18:17:21Z","timestamp":1672165041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-76663-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030766627","9783030766634"],"references-count":70,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-76663-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"19 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APF","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Privacy Forum","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apf2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/privacyforum.eu\/","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":"43","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":"9","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":"21% - 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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Virtual Event","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}