{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T10:38:37Z","timestamp":1762079917700,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T00:00:00Z","timestamp":1670716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation program","award":["825162"],"award-info":[{"award-number":["825162"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Clinical data analysis could lead to breakthroughs. However, clinical data contain sensitive information about participants that could be utilized for unethical activities, such as blackmailing, identity theft, mass surveillance, or social engineering. Data anonymization is a standard step during data collection, before sharing, to overcome the risk of disclosure. However, conventional data anonymization techniques are not foolproof and also hinder the opportunity for personalized evaluations. Much research has been done for synthetic data generation using generative adversarial networks and many other machine learning methods; however, these methods are either not free to use or are limited in capacity. This study evaluates the performance of an emerging tool named synthpop, an R package producing synthetic data as an alternative approach for data anonymization. This paper establishes data standards derived from the original data set based on the utilities and quality of information and measures variations in the synthetic data set to evaluate the performance of the data synthesis process. The methods to assess the utility of the synthetic data set can be broadly divided into two approaches: general utility and specific utility. General utility assesses whether synthetic data have overall similarities in the statistical properties and multivariate relationships with the original data set. Simultaneously, the specific utility assesses the similarity of a fitted model\u2019s performance on the synthetic data to its performance on the original data. The quality of information is assessed by comparing variations in entropy bits and mutual information to response variables within the original and synthetic data sets. The study reveals that synthetic data succeeded at all utility tests with a statistically non-significant difference and not only preserved the utilities but also preserved the complexity of the original data set according to the data standard established in this study. Therefore, synthpop fulfills all the necessities and unfolds a wide range of opportunities for the research community, including easy data sharing and information protection.<\/jats:p>","DOI":"10.3390\/data7120178","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T02:15:33Z","timestamp":1670811333000},"page":"178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Impacts of Data Synthesis: A Metric for Quantifiable Data Standards and Performances"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0051-0268","authenticated-orcid":false,"given":"Gunjan","family":"Chandra","sequence":"first","affiliation":[{"name":"Biomimetics and Intelligent Systems Group, Faculty of Information Technology and Electrical Engineering, University of Oulu, Pentti Kaiteran katu 1, 90570 Oulu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5995-5421","authenticated-orcid":false,"given":"Pekka","family":"Siirtola","sequence":"additional","affiliation":[{"name":"Biomimetics and Intelligent Systems Group, Faculty of Information Technology and Electrical Engineering, University of Oulu, Pentti Kaiteran katu 1, 90570 Oulu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5215-6889","authenticated-orcid":false,"given":"Satu","family":"Tamminen","sequence":"additional","affiliation":[{"name":"Biomimetics and Intelligent Systems Group, Faculty of Information Technology and Electrical Engineering, University of Oulu, Pentti Kaiteran katu 1, 90570 Oulu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0474-0033","authenticated-orcid":false,"given":"Mikael J.","family":"Knip","sequence":"additional","affiliation":[{"name":"Pediatric Research Center, Children\u2019s Hospital, University of Helsinki and Helsinki University Hospital, Yliopistonkatu 4, 00100 Helsinki, Finland"},{"name":"Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Yliopistonkatu 3, 00014 Helsinki, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6557-270X","authenticated-orcid":false,"given":"Riitta","family":"Veijola","sequence":"additional","affiliation":[{"name":"Department of Paediatrics, University of Oulu, Oulu University Hospital, Kajaanintie 50, 90220 Oulu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-8602","authenticated-orcid":false,"given":"Juha","family":"R\u00f6ning","sequence":"additional","affiliation":[{"name":"Biomimetics and Intelligent Systems Group, Faculty of Information Technology and Electrical Engineering, University of Oulu, Pentti Kaiteran katu 1, 90570 Oulu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1142\/S0218488502001648","article-title":"k-anonymity: A model for protecting privacy","volume":"10","author":"Sweeney","year":"2002","journal-title":"Int. J. Uncertain. Fuzziness Knowl.-Based Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1109\/JBHI.2015.2406883","article-title":"Big data, big knowledge: Big data for personalized healthcare","volume":"19","author":"Viceconti","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_3","first-page":"1701","article-title":"Broken promises of privacy: Responding to the surprising failure of anonymization","volume":"57","author":"Ohm","year":"2009","journal-title":"UCLA Law Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"252","DOI":"10.14745\/ccdr.v45i10a01","article-title":"Open Science\/Open Data: Reaping the benefits of Open Data in public health","volume":"45","author":"Huston","year":"2019","journal-title":"Can. Commun. Dis. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"63","DOI":"10.4103\/2229-3485.203036","article-title":"Data sharing: A viable resource for future","volume":"8","author":"Singh","year":"2017","journal-title":"Perspect. Clin. Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Devriendt, T., Borry, P., and Shabani, M. (2021). Factors that influence data sharing through data sharing platforms: A qualitative study on the views and experiences of cohort holders and platform developers. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0254202"},{"key":"ref_7","unstructured":"Yale, A., Dash, S., Dutta, R., Guyon, I., Pavao, A., and Bennett, K.P. (, January 24\u201326April). Privacy Preserving Synthetic Health Data. Proceedings of the 2019 ESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium."},{"key":"ref_8","unstructured":"(2022, October 21). Finnish Type 1 Diabetes Prediction and Prevention. Available online: http:\/\/dipp.fi."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9193","DOI":"10.1073\/pnas.87.23.9193","article-title":"Multisurface method of pattern separation for medical diagnosis applied to breast cytology","volume":"87","author":"Wolberg","year":"1990","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/S1386-5056(02)00007-2","article-title":"The computerized patient record: Balancing effort and benefit","volume":"65","year":"2002","journal-title":"Int. J. Med. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102","DOI":"10.4258\/hir.2021.27.2.102","article-title":"Review of national-level personal health records in advanced countries","volume":"27","author":"Lee","year":"2021","journal-title":"Healthc. Inform. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.semradonc.2019.05.010","article-title":"The evolving use of electronic health records (EHR) for research","volume":"Volume 29","author":"Kim","year":"2019","journal-title":"Proceedings of the Seminars in Radiation Oncology"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"El Emam, K., Jonker, E., Arbuckle, L., and Malin, B. (2011). A systematic review of re-identification attacks on health data. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0028071"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1146\/annurev.genom.7.080505.115721","article-title":"The Uneasy Ethical and Legal Underpinnings of Large-Scale Genomic Biobanks","volume":"8","author":"Greely","year":"2007","journal-title":"Annu. Rev. Genom. Hum. Genet."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1080\/01621459.1972.10481199","article-title":"On the question of statistical confidentiality","volume":"67","author":"Fellegi","year":"1972","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1145\/320613.320616","article-title":"Secure statistical databases with random sample queries","volume":"5","author":"Denning","year":"1980","journal-title":"ACM Trans. Database Syst. (TODS)"},{"key":"ref_17","unstructured":"Samarati, P., and Sweeney, L. (1998). Protecting Privacy When Disclosing Information: K-Anonymity and Its Enforcement through Generalization and Suppression, SRI Computer Science Laboratory. Technical Report SRI-CSL-98-04."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3-es","DOI":"10.1145\/1217299.1217302","article-title":"L-diversity: Privacy beyond k-anonymity","volume":"1","author":"Machanavajjhala","year":"2007","journal-title":"Assoc. Comput. Mach. Trans. Knowl. Discov. Data"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, N., Li, T., and Venkatasubramanian, S. (2007, January 15\u201320). t-closeness: Privacy beyond k-anonymity and l-diversity. Proceedings of the 2007 IEEE, 23rd International Conference on Data Engineering, Istanbul, Turkey.","DOI":"10.1109\/ICDE.2007.367856"},{"key":"ref_20","unstructured":"Dwork, C., McSherry, F., Nissim, K., and Smith, A. Calibrating noise to sensitivity in private data analysis. Proceedings of the Theory of Cryptography Conference."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Erlingsson, \u00da., Pihur, V., and Korolova, A. (2014, January 3\u20137). Rappor: Randomized aggregatable privacy-preserving ordinal response. Proceedings of the 2014 ACM, Special Interest Group on Security, Audit and Control (SIGSAC) Conference on Computer and Communications Security, Scottsdale, AZ, USA.","DOI":"10.1145\/2660267.2660348"},{"key":"ref_22","unstructured":"Press, I.A. (2022, December 04). Apple Previews iOS 10, the Biggest iOS Release Ever. Available online: https:\/\/www.apple.com\/newsroom\/2016\/06\/apple-previews-ios-10-biggest-ios-release-ever\/."},{"key":"ref_23","unstructured":"Muralidhar, K., Domingo-Ferrer, J., and Mart\u00ednez, S. epsilon-Differential Privacy for Microdata Releases Does Not Guarantee Confidentiality (Let Alone Utility). Proceedings of the International Conference on Privacy in Statistical Databases."},{"key":"ref_24","unstructured":"Culnane, C., Rubinstein, B.I., and Teague, V. (2017). Health data in an open world. arXiv."},{"key":"ref_25","unstructured":"gdpr.eu (2022, December 04). General Data Protection Regulation. Available online: https:\/\/gdpr.eu."},{"key":"ref_26","unstructured":"Tonic (2022, December 04). The Fake Data Company. Available online: https:\/\/www.tonic.ai."},{"key":"ref_27","unstructured":"Hazy Limited (2022, December 04). Synthetic Data. Real Results. Available online: https:\/\/hazy.com."},{"key":"ref_28","unstructured":"Datomize (2022, December 04). Limited Data. Unlimited Insights. Available online: https:\/\/www.datomize.com."},{"key":"ref_29","unstructured":"Mostly AI (2022, December 04). Smarter Synthetic Data. Available online: https:\/\/mostly.ai."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v074.i11","article-title":"Synthpop: Bespoke creation of synthetic data in R","volume":"74","author":"Nowok","year":"2016","journal-title":"J. Stat. Softw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1037\/pspp0000208","article-title":"Using 26,000 diary entries to show ovulatory changes in sexual desire and behavior","volume":"121","author":"Arslan","year":"2018","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1111\/rssa.12358","article-title":"General and specific utility measures for synthetic data","volume":"181","author":"Snoke","year":"2018","journal-title":"J. R. Stat. Soc. Ser. A Stat. Soc."},{"key":"ref_34","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/BF00116037","article-title":"The strength of weak learnability","volume":"5","author":"Schapire","year":"1990","journal-title":"Mach. Learn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1006\/inco.1995.1136","article-title":"Boosting a weak learning algorithm by majority","volume":"121","author":"Freund","year":"1995","journal-title":"Inf. Comput."},{"key":"ref_38","unstructured":"Freund, Y., and Schapire, R.E. (1996, January 3\u20136). Experiments with a new boosting algorithm. Proceedings of the 13th International Conference proceedings, Machine Learning, San Francisco, CA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors)","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_41","unstructured":"Click, C., Malohlava, M., Candel, A., Roark, H., and Parmar, V. (2017). Gradient boosting machine with H2O. H2O AI."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Friedman, J., Hastie, T., and Tibshirani, R. (2001). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_43","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_44","unstructured":"Oliver, D.I. (2014). Privacy Engineering: A Dataflow and Ontological Approach, CreateSpace Independent Publishing Platform."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Oliver, I., and Miche, Y. (2016, January 6\u20139). On the development of a metric for quality of information content over anonymised data-sets. Proceedings of the 2016 IEEE, 10th International Conference on the Quality of Information and Communications Technology (QUATIC), Lisbon, Portugal.","DOI":"10.1109\/QUATIC.2016.047"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"066138","DOI":"10.1103\/PhysRevE.69.066138","article-title":"Estimating mutual information","volume":"69","author":"Kraskov","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_47","first-page":"1849","article-title":"Estimation of R\u00e9nyi entropy and mutual information based on generalized nearest-neighbor graphs","volume":"23","year":"2010","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.aci.2018.08.003","article-title":"Classification assessment methods","volume":"17","author":"Tharwat","year":"2018","journal-title":"Appl. Comput. Inform."},{"key":"ref_49","unstructured":"Taylor, J. (1997). Introduction to Error Analysis, The Study of Uncertainties in Physical Measurements, University Science Book."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, H., and Ma, Y. (2013). Imbalanced Learning: Foundations, Algorithms, and Applications, John Wiley & Sons.","DOI":"10.1002\/9781118646106"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from Imbalanced Data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Quintana, D. (2019). Synthetic datasets: A non-technical primer for the behavioural sciences to promote reproducibility and hypothesis-generation. PsyArXiv.","DOI":"10.31234\/osf.io\/dmfb3"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0933-3657(02)00049-0","article-title":"Uniqueness of medical data mining","volume":"26","author":"Cios","year":"2002","journal-title":"Artif. Intell. Med."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1093\/jamia\/ocaa039","article-title":"Balancing health privacy, health information exchange and research in the context of the COVID-19 pandemic","volume":"27","author":"Lenert","year":"2020","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/s41591-020-0832-5","article-title":"On the responsible use of digital data to tackle the COVID-19 pandemic","volume":"26","author":"Ienca","year":"2020","journal-title":"Nat. Med."},{"key":"ref_56","unstructured":"Dua, D., and Graff, C. (2022, December 04). UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/ml\/index.php."},{"key":"ref_57","unstructured":"Chandra, G. (2020). Impacts of Data Synthesis: A Metric for Quantifiable Data Standards and Performances. [Master\u2019s Thesis, University of Oulu]."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1056\/NEJM199207303270505","article-title":"Breast cancer","volume":"327","author":"Harris","year":"1992","journal-title":"N. Engl. J. Med."},{"key":"ref_59","unstructured":"Diabetesliitto (2022, December 04). Finnish Diabetes Association. Available online: https:\/\/www.diabetes.fi."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/7\/12\/178\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:38:08Z","timestamp":1760146688000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/7\/12\/178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,11]]},"references-count":59,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["data7120178"],"URL":"https:\/\/doi.org\/10.3390\/data7120178","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2022,12,11]]}}}