{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:48:58Z","timestamp":1758271738379,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030932053"},{"type":"electronic","value":"9783030932060"}],"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\/978-3-030-93206-0_3","type":"book-chapter","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T14:04:49Z","timestamp":1639663489000},"page":"32-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced Mixup Training: a\u00a0Defense Method Against Membership Inference Attack"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7207-2343","authenticated-orcid":false,"given":"Zongqi","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guowen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,12,17]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","unstructured":"Jia, J., Salem, A., Backes, M., Zhang, Y., Gong, N.Z.: MemGuard. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (2019). https:\/\/doi.org\/10.1145\/3319535.3363201","DOI":"10.1145\/3319535.3363201"},{"key":"3_CR2","unstructured":"Song, L., Mittal, P.: Systematic evaluation of privacy risks of machine learning models. In 30th USENIX Security Symposium (USENIX Security 21) (2021)"},{"key":"3_CR3","first-page":"1","volume":"2018","author":"H Zhang","year":"2017","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. Proc. ICLR 2018, 1\u201313 (2017)","journal-title":"Proc. ICLR"},{"key":"3_CR4","doi-asserted-by":"publisher","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP) (2017). https:\/\/doi.org\/10.1109\/sp.2017.41","DOI":"10.1109\/sp.2017.41"},{"key":"3_CR5","doi-asserted-by":"publisher","unstructured":"Nasr, M., Shokri, R., Houmansadr, A.: Machine learning with membership privacy using adversarial regularization. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (2018). https:\/\/doi.org\/10.1145\/3243734.3243855","DOI":"10.1145\/3243734.3243855"},{"key":"3_CR6","doi-asserted-by":"publisher","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: Proceedings 2019 Network and Distributed System Security Symposium (2019). https:\/\/doi.org\/10.14722\/ndss.2019.23119","DOI":"10.14722\/ndss.2019.23119"},{"key":"3_CR7","doi-asserted-by":"publisher","unstructured":"Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015). https:\/\/doi.org\/10.1145\/2810103.2813687","DOI":"10.1145\/2810103.2813687"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (2016). https:\/\/doi.org\/10.1145\/2976749.2978318","DOI":"10.1145\/2976749.2978318"},{"key":"3_CR9","doi-asserted-by":"publisher","unstructured":"Yeom, S., Giacomelli, I., Fredrikson, M., Jha, S.: Privacy risk in machine learning: analyzing the connection to overfitting (2018). In: 2018 IEEE 31st Computer Security Foundations Symposium (CSF) (2018). https:\/\/doi.org\/10.1109\/csf.2018.00027","DOI":"10.1109\/csf.2018.00027"},{"key":"3_CR10","unstructured":"Leino, K., Fredrikson, M.: Stolen memories: leveraging model memorization for calibrated white-box membership inference. In: 29th USENIX Security Symposium (USENIX Security 20), pp. 1605\u20131622 (2020)"},{"key":"3_CR11","doi-asserted-by":"publisher","unstructured":"Bassily, R., Smith, A., Thakurta, A.: Private empirical risk minimization: efficient algorithms and tight error bounds. In: 2014 IEEE 55th Annual Symposium on Foundations of Computer Science (2014). https:\/\/doi.org\/10.1109\/focs.2014.56","DOI":"10.1109\/focs.2014.56"},{"key":"3_CR12","doi-asserted-by":"publisher","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (2012). https:\/\/doi.org\/10.1109\/cvpr.2012.6248074","DOI":"10.1109\/cvpr.2012.6248074"},{"issue":"1","key":"3_CR13","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/s0933-3657(01)00077-x","volume":"23","author":"I Kononenko","year":"2001","unstructured":"Kononenko, I.: Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Medl. 23(1), 89\u2013109 (2001). https:\/\/doi.org\/10.1016\/s0933-3657(01)00077-x","journal-title":"Artif. Intell. Medl."},{"key":"3_CR14","unstructured":"Amodei, D., et al.: Deep speech 2: end-to-end speech recognition in English and mandarin. In: International Conference on Machine Learning, pp. 173\u2013182. PMLR, June 2016"},{"issue":"1","key":"3_CR15","doi-asserted-by":"publisher","first-page":"133","DOI":"10.2478\/popets-2019-0008","volume":"2019","author":"J Hayes","year":"2018","unstructured":"Hayes, J., Melis, L., Danezis, G., De Cristofaro, E.: LOGAN: membership inference attacks against generative models. Proc. Privacy Enhan. Technol. 2019(1), 133\u2013152 (2018). https:\/\/doi.org\/10.2478\/popets-2019-0008","journal-title":"Proc. Privacy Enhan. Technol."},{"key":"3_CR16","unstructured":"Zhang, L., Deng, Z., Kawaguchi, K., Ghorbani, A., Zou, J.: How does mixup help with robustness and generalization. In: Proceedings of ICLR (2021)"},{"key":"3_CR17","first-page":"911","volume":"15","author":"X Guowen","year":"2019","unstructured":"Guowen, X., Li, H., Liu, S., Yang, K., Lin, X.: VerifyNet: secure and verifiable federated learning. IEEE Trans. Inf. Forens. Secur. 15, 911\u2013926 (2019)","journal-title":"IEEE Trans. Inf. Forens. Secur."},{"key":"3_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.3005909","author":"X Guowen","year":"2020","unstructured":"Guowen, X., Li, H., Zhang, Y., Shengmin, X., Ning, J., Deng, R.H.: Priva-cy-preserving federated deep learning with irregular users. IEEE Trans. Depend. Secur. Comput. (2020). https:\/\/doi.org\/10.1109\/TDSC.2020.3005909","journal-title":"IEEE Trans. Depend. Secur. Comput."}],"container-title":["Lecture Notes in Computer Science","Information Security Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93206-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T06:03:24Z","timestamp":1643004204000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93206-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030932053","9783030932060"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93206-0_3","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":"17 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISPEC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Security Practice and Experience","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ispec2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ispec2021.nuist.edu.cn","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"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":"94","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":"23","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":"24% - 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":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","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":"Doble peer review","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)"}}]}}