{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T05:18:09Z","timestamp":1719551889281},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,25]]},"abstract":"<jats:p>Synthetic data has been more and more used in the last few years. While its applications are various, measuring its utility and privacy is seldom an easy task. Since there are different methods of evaluating these issues, which are dependent on data types, use cases and purpose, a generic method for evaluating utility and privacy does not exist at the moment. So, we introduced a compilation of the most recent methods for evaluating privacy and utility into a single executable in order to create a report of the similarities and potential privacy breaches between two datasets, whether it is related to synthetic or not. We catalogued 24 different methods, from qualitative to quantitative, column-wise or table-wise evaluations. We hope this resource can help scientists and industries get a better grasp of the synthetic data they have and produce more easily and a better basis to create a new, more broad method for evaluating dataset similarities.<\/jats:p>","DOI":"10.3233\/shti220389","type":"book-chapter","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:12:34Z","timestamp":1653480754000},"source":"Crossref","is-referenced-by-count":1,"title":["Dataset Comparison Tool: Utility and Privacy"],"prefix":"10.3233","author":[{"given":"Jo\u00e3o","family":"Coutinho-Almeida","sequence":"first","affiliation":[{"name":"CINTESIS \u2013 Centre for Health Technologies and Services Research, University of Porto, Portugal"},{"name":"Health Data Science PhD Program, Faculty of Medicine of the University of Porto, Portugal"}]},{"given":"Ricardo Jo\u00e3o","family":"Cruz-Correia","sequence":"additional","affiliation":[{"name":"MEDCIDS \u2013 Faculty of Medicine of University of Porto, Portugal"}]},{"given":"Pedro Pereira","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"MEDCIDS \u2013 Faculty of Medicine of University of Porto, Portugal"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Challenges of Trustable AI and Added-Value on Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220389","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:12:34Z","timestamp":1653480754000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220389"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,25]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220389","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,25]]}}}