{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:23:39Z","timestamp":1742912619600,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031682070"},{"type":"electronic","value":"9783031682087"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-68208-7_19","type":"book-chapter","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T06:02:44Z","timestamp":1723615364000},"page":"224-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Experimental Evaluation for\u00a0Risk Assessment of\u00a0Privacy Preserving Synthetic Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7705-5996","authenticated-orcid":false,"given":"Koji","family":"Chida","sequence":"first","affiliation":[]},{"given":"Susumu","family":"Kakuta","sequence":"additional","affiliation":[]},{"given":"Hiroyuki","family":"Itakura","sequence":"additional","affiliation":[]},{"given":"Ichiro","family":"Ishihara","sequence":"additional","affiliation":[]},{"given":"Kosuke","family":"Yoshioka","sequence":"additional","affiliation":[]},{"given":"Hiroshi","family":"Takeuchi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"unstructured":"The G7 Data Protection and Privacy Authorities. G7 DPAs\u2019 Communiqu\u00e9: Roundtable of G7 Data Protection and Privacy Authorities \u2013 Working toward operationalizing Data Free Flow with Trust and intensifying regulatory cooperation, 21 June (2023). https:\/\/www.ppc.go.jp\/files\/pdf\/G7roundtable_202306_communique.pdf","key":"19_CR1"},{"unstructured":"National Science and Technology Council. National Strategy to Advance Privacy-Preserving Data Sharing and Analytics, March (2023). https:\/\/www.whitehouse.gov\/wp-content\/uploads\/2023\/03\/National-Strategy-to-Advance-Privacy-Preserving-Data-Sharing-and-Analytics.pdf","key":"19_CR2"},{"unstructured":"United Nations Committee of Experts on Big Data and Data Science for Official Statistics. The United Nations Guide on Privacy-Enhancing Technologies for Official Statistics (2023). https:\/\/unstats.un.org\/bigdata\/task-teams\/privacy\/guide\/indx.cshtml","key":"19_CR3"},{"unstructured":"OECD. Emerging Privacy Enhancing Technologies \u2013 Current regulatory and policy approaches. Number 351 in OECD Digital Economy Papers. OECD publishing, 08 March (2023). https:\/\/www.oecd-ilibrary.org\/content\/paper\/bf121be4-en","key":"19_CR4"},{"key":"19_CR5","first-page":"2672","volume":"2014","author":"IJ Goodfellow","year":"2014","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. NIPS 2014, 2672\u20132680 (2014)","journal-title":"NIPS"},{"unstructured":"Houssiau, F., et al.: TAPAS: a toolbox for adversarial privacy auditing of synthetic data. In: NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research (2022). https:\/\/openreview.net\/pdf?id=9hXskf1K7zQ","key":"19_CR6"},{"doi-asserted-by":"crossref","unstructured":"Nezhad, F.H., Rotalinti, Y., Myles, P., Tucker, A.: Privacy assessment of synthetic patient data. In: CBMS 2023, pp. 1\u20136. IEEE (2023)","key":"19_CR7","DOI":"10.1109\/CBMS58004.2023.00182"},{"unstructured":"Dave, K.: Adversarial privacy auditing of synthetically generated data produced by large language models using the TAPAS toolbox. UCLA Electronic Theses and Dissertations (2024). https:\/\/escholarship.org\/uc\/item\/6rw664ww","key":"19_CR8"},{"key":"19_CR9","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/11681878_14","volume-title":"Theory of Cryptography","author":"C Dwork","year":"2006","unstructured":"Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating Noise to Sensitivity in Private Data Analysis. In: Halevi, S., Rabin, T. (eds.) Theory of Cryptography, pp. 265\u2013284. Springer Berlin Heidelberg, Berlin, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11681878_14"},{"doi-asserted-by":"publisher","unstructured":"Task, C., Bhagat, K., Howarth, G.: SDNist v2: deidentified data report tool. National Institute of Standards and Technology (2023). https:\/\/doi.org\/10.18434\/mds2-2943","key":"19_CR10","DOI":"10.18434\/mds2-2943"},{"issue":"2","key":"19_CR11","first-page":"312","volume":"2023","author":"M Giomi","year":"2023","unstructured":"Giomi, M., Boenisch, F., Wehmeyer, C., Tasn\u00e1di, B.: A unified framework for quantifying privacy risk in synthetic data. PETS 2023(2), 312\u2013328 (2023)","journal-title":"PETS"},{"unstructured":"van Breugel, B., Sun, H., Qian, Z., van der Schaar, M.: Membership inference attacks against synthetic data through overfitting detection. In: AISTATS 2023, Machine Learning Research 206, pp. 3493\u20133514. PMLR (2023)","key":"19_CR12"},{"unstructured":"Stadler, T., Oprisanu, B., Troncoso, C.: Synthetic Data - anonymisation groundhog day. USENIX Security 2022, pp. 1451\u20131468. USENIX Association (2022)","key":"19_CR13"},{"doi-asserted-by":"crossref","unstructured":"Nasr, M., Song, S., Thakurta, A., Papernot, N., Carlini, N.: Adversary instantiation: lower bounds for differentially private machine learning. In: IEEE Symposium on Security and Privacy 2021, pp. 866\u2013882. IEEE (2021)","key":"19_CR14","DOI":"10.1109\/SP40001.2021.00069"},{"issue":"6","key":"19_CR15","doi-asserted-by":"publisher","first-page":"4037","DOI":"10.1109\/TIT.2017.2685505","volume":"63","author":"P Kairouz","year":"2017","unstructured":"Kairouz, P., Oh, S., Viswanath, P.: The composition theorem for differential privacy. IEEE Trans. Inf. Theory 63(6), 4037\u20134049 (2017). https:\/\/doi.org\/10.1109\/TIT.2017.2685505","journal-title":"IEEE Trans. Inf. Theory"},{"unstructured":"McKenna, R., Sheldon, D., Miklau, G.: Graphical-model based estimation and inference for differential privacy. In: ICML 2019, Machine Learning Research 97, pp. 4435\u20134444. PMLR (2019)","key":"19_CR16"},{"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 2014, pp. 1423\u20131434. ACM (2014)","key":"19_CR17","DOI":"10.1145\/2588555.2588573"},{"unstructured":"Li, H., Xiong, L., Jiang, X.: Differentially private synthetization of multi-dimensional data using copula functions. In: EDBT 2014, pp. 475\u2013486 (2014). OpenProceedings.org","key":"19_CR18"},{"issue":"309","key":"19_CR19","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1080\/01621459.1965.10480775","volume":"60","author":"SL Warner","year":"1965","unstructured":"Warner, S.L.: Randomized Response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60(309), 63\u201369 (1965)","journal-title":"J. Am. Stat. Assoc."},{"issue":"3\u20134","key":"19_CR20","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."}],"container-title":["Lecture Notes in Computer Science","Modeling Decisions for Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-68208-7_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T06:08:10Z","timestamp":1723615690000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-68208-7_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031682070","9783031682087"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-68208-7_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"15 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MDAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Modeling Decisions for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mdai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}