{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T03:54:39Z","timestamp":1771300479066,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819608133","type":"print"},{"value":"9789819608140","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-0814-0_20","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T17:29:57Z","timestamp":1734024597000},"page":"301-315","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Secure Privacy-Preserving SMOTE for\u00a0Vertical Federated Learning"],"prefix":"10.1007","author":[{"given":"Wenyou","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haihang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaming","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanglei","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuming","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","unstructured":"Arafa, A., El-Fishawy, N., Badawy, M., Radad, M.: RN-SMOTE: reduced noise smote based on DBSCAN for enhancing imbalanced data classification. J. King Saud Univ. Comput. Inf. Sci. 34(8, Part A), 5059\u20135074 (2022). https:\/\/doi.org\/10.1016\/j.jksuci.2022.06.005","DOI":"10.1016\/j.jksuci.2022.06.005"},{"key":"20_CR2","doi-asserted-by":"publisher","unstructured":"Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. ACM (1) (2004). https:\/\/doi.org\/10.1145\/1007730.1007735","DOI":"10.1145\/1007730.1007735"},{"key":"20_CR3","doi-asserted-by":"publisher","unstructured":"Bian, K., Zheng, H.: FedAvg-DWA: a novel algorithm for enhanced fraud detection in federated learning environment. In: 2023 4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), pp. 13\u201317 (2023). https:\/\/doi.org\/10.1109\/ICBAIE59714.2023.10281317","DOI":"10.1109\/ICBAIE59714.2023.10281317"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Bunkhumpornpat, C., Sinapiromsaran, K., Lursinsap, C.: Safe-level-smote: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: Advances in Knowledge Discovery and Data Mining, pp. 475\u2013482. Springer Berlin Heidelberg, Berlin, Heidelberg (2009)","DOI":"10.1007\/978-3-642-01307-2_43"},{"issue":"12","key":"20_CR5","doi-asserted-by":"publisher","first-page":"3663","DOI":"10.1109\/TMI.2022.3192483","volume":"41","author":"Z Chen","year":"2022","unstructured":"Chen, Z., Yang, C., Zhu, M., Peng, Z., Yuan, Y.: Personalized retrogress-resilient federated learning toward imbalanced medical data. IEEE Trans. Med. Imaging 41(12), 3663\u20133674 (2022). https:\/\/doi.org\/10.1109\/TMI.2022.3192483","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR6","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1145\/772862.772867","volume":"4","author":"C Clifton","year":"2002","unstructured":"Clifton, C., Kantarcioglu, M., Vaidya, J.: Tools for privacy preserving distributed data mining. ACM SIGKDD Explor. Newsl. 4, 28\u201334 (2002). https:\/\/doi.org\/10.1145\/772862.772867","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"20_CR7","doi-asserted-by":"publisher","unstructured":"Dai, W., et al.: TEE: a virtual DRTM based execution environment for secure cloud-end computing. Futur. Gener. Comput. Syst. Int. J. e-Sci. 49, 47\u201357 (2015). https:\/\/doi.org\/10.1016\/j.future.2014.08.005","DOI":"10.1016\/j.future.2014.08.005"},{"key":"20_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2018.06.056","volume":"465","author":"G Douzas","year":"2018","unstructured":"Douzas, G., Bacao, F., Last, F.: Improving imbalanced learning through a heuristic oversampling method based on k-means and smote. Inf. Sci. 465, 1\u201320 (2018). https:\/\/doi.org\/10.1016\/j.ins.2018.06.056","journal-title":"Inf. Sci."},{"key":"20_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110986","volume":"150","author":"J Guo","year":"2024","unstructured":"Guo, J., Wu, H., Chen, X., Lin, W.: Adaptive SV-borderline smote-SVM algorithm for imbalanced data classification. Appl. Soft Comput. 150, 110986 (2024). https:\/\/doi.org\/10.1016\/j.asoc.2023.110986","journal-title":"Appl. Soft Comput."},{"key":"20_CR10","doi-asserted-by":"publisher","unstructured":"Han, H., Wang, W.Y., Mao, B.H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. Lecture Notes in Computer Science (2005). https:\/\/doi.org\/10.1007\/1153805_91","DOI":"10.1007\/1153805_91"},{"key":"20_CR11","doi-asserted-by":"publisher","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322\u20131328 (2008). https:\/\/doi.org\/10.1109\/IJCNN.2008.4633969","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"20_CR12","doi-asserted-by":"publisher","unstructured":"Konen, J., Mcmahan, H.B., Ramage, D., Richt\u00e1rik, P.: Federated optimization: distributed machine learning for on-device intelligence (2016). https:\/\/doi.org\/10.48550\/arXiv.1610.02527","DOI":"10.48550\/arXiv.1610.02527"},{"key":"20_CR13","doi-asserted-by":"publisher","unstructured":"Konen, J., Mcmahan, H.B., Yu, F.X., Richt\u00e1rik, P., Bacon, D.: Federated learning: strategies for improving communication efficiency (2016). https:\/\/doi.org\/10.48550\/arXiv.1610.05492","DOI":"10.48550\/arXiv.1610.05492"},{"key":"20_CR14","doi-asserted-by":"publisher","first-page":"114692","DOI":"10.1109\/ACCESS.2020.3003346","volume":"8","author":"I Kunakorntum","year":"2020","unstructured":"Kunakorntum, I., Hinthong, W., Phunchongharn, P.: A synthetic minority based on probabilistic distribution (SyMProD) oversampling for imbalanced datasets. IEEE Access 8, 114692\u2013114704 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3003346","journal-title":"IEEE Access"},{"issue":"1","key":"20_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2023.100893","volume":"5","author":"VC Pezoulas","year":"2024","unstructured":"Pezoulas, V.C., Kalatzis, F., Exarchos, T.P., Goules, A., Tzioufas, A.G., Fotiadis, D.I.: FHBF: federated hybrid boosted forests with dropout rates for supervised learning tasks across highly imbalanced clinical datasets. Patterns 5(1), 100893 (2024). https:\/\/doi.org\/10.1016\/j.patter.2023.100893","journal-title":"Patterns"},{"key":"20_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0465-6","author":"E Ramentol","year":"2012","unstructured":"Ramentol, E., Caballero, Y., Bello, R., Herrera, F.: SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory. Knowl. Inf. Syst. (2012). https:\/\/doi.org\/10.1007\/s10115-011-0465-6","journal-title":"Knowl. Inf. Syst."},{"key":"20_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121848","volume":"238","author":"P Sun","year":"2024","unstructured":"Sun, P., Wang, Z., Jia, L., Xu, Z.: SMOTE-kTLNN: a hybrid re-sampling method based on smote and a two-layer nearest neighbor classifier. Expert Syst. Appl. 238, 121848 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.121848","journal-title":"Expert Syst. Appl."},{"key":"20_CR18","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.ins.2014.08.051","volume":"291","author":"JA S\u00e1ez","year":"2015","unstructured":"S\u00e1ez, J.A., Luengo, J., Stefanowski, J., Herrera, F.: SMOTE-IPF: addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Inf. Sci. 291, 184\u2013203 (2015). https:\/\/doi.org\/10.1016\/j.ins.2014.08.051","journal-title":"Inf. Sci."},{"key":"20_CR19","doi-asserted-by":"publisher","unstructured":"Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2) (2019). https:\/\/doi.org\/10.1145\/3298981","DOI":"10.1145\/3298981"},{"key":"20_CR20","doi-asserted-by":"publisher","unstructured":"Yao, A.C.C.: How to generate and exchange secrets. In: 27th Annual Symposium on Foundations of Computer Science (SFCS 1986), pp. 162\u2013167 (1986). https:\/\/doi.org\/10.1109\/SFCS.1986.25","DOI":"10.1109\/SFCS.1986.25"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0814-0_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T18:06:36Z","timestamp":1734026796000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0814-0_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"ISBN":["9789819608133","9789819608140"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0814-0_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","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":"3 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}