{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T00:13:18Z","timestamp":1768695198224,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62206238"],"award-info":[{"award-number":["62206238"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20220562"],"award-info":[{"award-number":["BK20220562"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["22KJB520010"],"award-info":[{"award-number":["22KJB520010"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s00521-023-08593-y","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T14:28:14Z","timestamp":1682000894000},"page":"17013-17027","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multi-level membership inference attacks in federated Learning based on active GAN"],"prefix":"10.1007","volume":"35","author":[{"given":"Hao","family":"Sui","sequence":"first","affiliation":[]},{"given":"Xiaobing","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2143-5666","authenticated-orcid":false,"given":"Jiale","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Wenjuan","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"issue":"3","key":"8593_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-01585-4","volume":"13","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Cheng Y, Kang Y, Chen T, Yu H (2019) Federated learning. Synth Lect Artif Intell Machine Learn 13(3):1\u2013207","journal-title":"Synth Lect Artif Intell Machine Learn"},{"key":"8593_CR2","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data, Artif Intell Stat, 1273-1282"},{"issue":"3","key":"8593_CR3","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Talwalkar A, Smith V (2020) Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag 37(3):50\u201360","journal-title":"IEEE Signal Process Mag"},{"issue":"9","key":"8593_CR4","doi-asserted-by":"publisher","first-page":"3400","DOI":"10.1109\/TNNLS.2019.2944481","volume":"31","author":"F Sattler","year":"2019","unstructured":"Sattler F, Wiedemann S, M\u00fcller KR, Samek W (2019) Robust and communication-efficient federated learning from non-iid data. IEEE Transactions on neural networks and learning systems 31(9):3400\u20133413","journal-title":"IEEE Transactions on neural networks and learning systems"},{"key":"8593_CR5","doi-asserted-by":"crossref","unstructured":"Melis L, Song C, Cristofaro ED, Shmatikov V (2019) Exploiting unintended feature leakage in collaborative learning, IEEE Symp Secur Privacy (SP) 691-706","DOI":"10.1109\/SP.2019.00029"},{"key":"8593_CR6","doi-asserted-by":"crossref","unstructured":"Hitaj B, Ateniese G, Perez-Cruz F (2017) Deep models under the GAN: information leakage from collaborative deep learning, In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, pp 603-618","DOI":"10.1145\/3133956.3134012"},{"key":"8593_CR7","unstructured":"Zhu L, Liu Z, Han S (2019) Deep leakage from gradients, Advances in neural information processing systems, 32"},{"key":"8593_CR8","doi-asserted-by":"crossref","unstructured":"Chen J, Zhang J, Zhao Y, Han H, Zhu Kun, Chen Bing (2020) Beyond model-level membership privacy leakage: an adversarial approach in federated learning, International Conference on Computer Communications and Networks, pp 1-9","DOI":"10.1109\/ICCCN49398.2020.9209744"},{"issue":"23","key":"8593_CR9","doi-asserted-by":"publisher","first-page":"17333","DOI":"10.1007\/s00521-020-04880-0","volume":"32","author":"S Nikolaidis","year":"2020","unstructured":"Nikolaidis S, Refanidis I (2020) Privacy preserving distributed training of neural networks. Neural Comput Appl 32(23):17333\u201317350","journal-title":"Neural Comput Appl"},{"key":"8593_CR10","doi-asserted-by":"crossref","unstructured":"Shokri R, Stronati M, Song C, Shmatikov V (2017) Membership inference attacks against machine learning models, IEEE symposium on security and privacy (SP), pp 3-18","DOI":"10.1109\/SP.2017.41"},{"key":"8593_CR11","doi-asserted-by":"crossref","unstructured":"Song L, Shokri R, Mittal P (2019) Privacy risks of securing machine learning models against adversarial examples, In: Proceedings of the 2019 ACM SIGSAC conference on computer and communications security, pp 241-257","DOI":"10.1145\/3319535.3354211"},{"key":"8593_CR12","unstructured":"Song L, Mittal P (2020) Systematic evaluation of privacy risks of machine learning models, arXiv preprint arXiv:2003.10595"},{"key":"8593_CR13","doi-asserted-by":"crossref","unstructured":"Yeom S, Giacomelli I, Fredrikson M, Jha S (2018) Privacy risk in machine learning: Analyzing the connection to overfitting, 2018 IEEE 31st Computer security foundations symposium (CSF), pp 268-282","DOI":"10.1109\/CSF.2018.00027"},{"key":"8593_CR14","doi-asserted-by":"crossref","unstructured":"Nasr M, Shokri R, Houmansadr A (2019) Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning, IEEE symposium on security and privacy (SP), IEEE, pp 739-753","DOI":"10.1109\/SP.2019.00065"},{"key":"8593_CR15","unstructured":"Mehdi M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784"},{"issue":"17","key":"8593_CR16","doi-asserted-by":"publisher","first-page":"14143","DOI":"10.1007\/s00521-020-04809-7","volume":"32","author":"X Zhang","year":"2020","unstructured":"Zhang X, Jing XY, Zhu X, Ma F (2020) Semi-supervised person re-identification by similarity-embedded cycle GANs. Neural Comput Appl 32(17):14143\u201314152","journal-title":"Neural Comput Appl"},{"key":"8593_CR17","doi-asserted-by":"crossref","unstructured":"Vaidya J, Clifton C (2002) Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 639-644","DOI":"10.1145\/775047.775142"},{"issue":"2","key":"8593_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol TIST 10(2):1\u201319","journal-title":"ACM Trans Intell Syst Technol TIST"},{"issue":"4","key":"8593_CR19","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/MIS.2020.2988525","volume":"35","author":"Y Liu","year":"2020","unstructured":"Liu Y, Kang Y, Xing C, Chen T, Yang Q (2020) A secure federated transfer learning framework. IEEE Intell Syst 35(4):70\u201382","journal-title":"IEEE Intell Syst"},{"key":"8593_CR20","unstructured":"Fredrikson M, Lantz E, Jha S, Lin S, Page D, Ristenpart T (2014) Privacy in pharmacogenetics: An End-to-End case study of personalized warfarin dosing, 23rd USENIX Security Symposium USENIX Security 14, pp 17-32"},{"key":"8593_CR21","doi-asserted-by":"crossref","unstructured":"Fredrikson M, Jha S, Ristenpart T (2015) Model inversion attacks that exploit confidence information and basic countermeasures, In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp 1322-1333","DOI":"10.1145\/2810103.2813677"},{"key":"8593_CR22","unstructured":"Yang C, Wu Q, Li H, Chen Y (2017) Generative poisoning attack method against neural networks, arXiv preprint arXiv:1703.01340"},{"key":"8593_CR23","doi-asserted-by":"crossref","unstructured":"Zhang J, Chen J, Wu D, Chen B, Yu S, (2019) Poisoning attack in federated learning using generative adversarial nets,\u201d in, (2019) 18th IEEE International Conference on trust, security and privacy in computing and communications\/13th IEEE International conference on big data science and engineering (TrustCom\/BigDataSE). IEEE pp 374\u2013380","DOI":"10.1109\/TrustCom\/BigDataSE.2019.00057"},{"key":"8593_CR24","doi-asserted-by":"crossref","unstructured":"Salem A, Zhang Y, Humbert M, Berrang P, Fritz M, Backes M (2018) Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models, arXiv preprint arXiv:1806.01246","DOI":"10.14722\/ndss.2019.23119"},{"key":"8593_CR25","unstructured":"Suri A, Kanani P, Marathe VJ, Peterson D (2022) Subject membership inference attacks in federated learning, arXiv preprint arXiv:2206.03317"},{"key":"8593_CR26","volume":"67","author":"Y Gu","year":"2022","unstructured":"Gu Y, Bai Y, Xu S (2022) CS-MIA: membership inference attack based on prediction confidence series in federated learning. J Inform Secur Appl 67:103201","journal-title":"J Inform Secur Appl"},{"issue":"3","key":"8593_CR27","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1002\/j.2168-9830.2004.tb00809.x","volume":"93","author":"M Prince","year":"2004","unstructured":"Prince M (2004) Does active learning work? a review of the research. J Eng Educ 93(3):223\u2013231","journal-title":"J Eng Educ"},{"issue":"19","key":"8593_CR28","doi-asserted-by":"publisher","first-page":"12535","DOI":"10.1007\/s00521-021-05896-w","volume":"33","author":"T Cai","year":"2021","unstructured":"Cai T, Ma Z, Zheng H, Zhou Y (2021) NE-LP: normalized entropy-and loss prediction-based sampling for active learning in Chinese word segmentation on EHRs. Neural Comput Appl 33(19):12535\u201312549","journal-title":"Neural Comput Appl"},{"key":"8593_CR29","unstructured":"Zhu J, Bento J (2017) Generative adversarial active learning. arXiv preprint arXiv:1702.07956"},{"key":"8593_CR30","unstructured":"Mottaghi A, Yeung S (2019) Adversarial representation active learning. arXiv preprint arXiv:1912.09720"},{"issue":"3","key":"8593_CR31","first-page":"30","volume":"7","author":"D Yang","year":"2016","unstructured":"Yang D, Zhang D, Qu B (2016) Participatory cultural mapping based on collective behavior data in location-based social networks. ACM TIST 7(3):30","journal-title":"ACM TIST"},{"issue":"6","key":"8593_CR32","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng L (2012) The mnist database of handwritten digit images for machine learning research best of the web. IEEE Signal Process Mag 29(6):141\u2013142","journal-title":"IEEE Signal Process Mag"},{"key":"8593_CR33","unstructured":"Krizhevsky A (2009) Learning multiple layers of features from tiny images, Master\u2019thesis, University of Toronto"},{"key":"8593_CR34","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V (2020) Federated optimization in heterogeneous networks. Proc Machine Learn Syst 2:429\u2013450","journal-title":"Proc Machine Learn Syst"},{"key":"8593_CR35","unstructured":"Li X, Huang K, Yang W, Wang S, Zhang Z (2019) On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08593-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08593-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08593-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T19:10:03Z","timestamp":1689189003000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08593-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,20]]},"references-count":35,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["8593"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08593-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,20]]},"assertion":[{"value":"9 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}