{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T20:07:22Z","timestamp":1780344442398,"version":"3.54.1"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2241213"],"award-info":[{"award-number":["U2241213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangxi Key Research and Development Project","award":["AB25069120"],"award-info":[{"award-number":["AB25069120"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272038"],"award-info":[{"award-number":["62272038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tifs.2026.3694646","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T19:45:22Z","timestamp":1779133522000},"page":"4970-4984","source":"Crossref","is-referenced-by-count":0,"title":["Toward More Practical Label Inference Attacks Against Graph-Based Vertical Federated Learning"],"prefix":"10.1109","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1547-1177","authenticated-orcid":false,"given":"Yimin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5786-1512","authenticated-orcid":false,"given":"Peng","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3277-3887","authenticated-orcid":false,"given":"Liehuang","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","volume":"54","author":"McMahan"},{"key":"ref2","article-title":"Federated learning for mobile keyboard prediction","author":"Hard","year":"2018","journal-title":"arXiv:1811.03604"},{"key":"ref3","article-title":"VFedMH: Vertical federated learning for training multi-party heterogeneous models","author":"Wang","journal-title":"arXiv:2310.13367"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3575637.3575644"},{"key":"ref5","first-page":"1397","article-title":"Label inference attacks against vertical federated learning","volume-title":"Proc. 31st USENIX Security Symp. (USENIX Secur.)","author":"Fu"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3559613.3563201"},{"key":"ref7","article-title":"Label inference attack against split learning under regression setting","author":"Xie","year":"2023","journal-title":"arXiv:2301.07284"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/tifs.2024.3356821"},{"key":"ref9","article-title":"Blindsage: Label inference attacks against node-level vertical federated graph neural networks","author":"Arazzi","year":"2023","journal-title":"arXiv:2308.02465"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_9"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134061"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228895"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2106.11593"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526127"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3161016"},{"key":"ref17","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Conf. Neural Inf. Process. Syst. (NeurIPS)","volume":"33","author":"Hu"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.52202\/079017-2838"},{"key":"ref19","first-page":"32552","article-title":"Pairwise alignment improves graph domain adaptation","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02206"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02257"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_10"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186116"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i14.29479"},{"key":"ref25","first-page":"45516","article-title":"Graph contrastive learning with stable and scalable spectral encoding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bo"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599546"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01395"},{"key":"ref28","first-page":"2988","article-title":"Asymmetric tri-training for unsupervised domain adaptation","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","volume":"70","author":"Saito"},{"key":"ref29","first-page":"1647","article-title":"Conditional adversarial domain adaptation","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Long"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00851"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_10"},{"key":"ref32","first-page":"14887","article-title":"Graph adversarial self-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst., Annu. Conf. Neural Inf. Process. Syst.","volume":"34","author":"Yang"},{"key":"ref33","first-page":"78190","article-title":"Integrating intermediate layer optimization and projected gradient descent for solving inverse problems with diffusion models","volume-title":"Proc. 42nd Int. Conf. Mach. Learn.","author":"Yang"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380219"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1460"},{"key":"ref36","article-title":"Online GNN evaluation under test-time graph distribution shifts","author":"Zheng","year":"2024","journal-title":"arXiv:2403.09953"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1402008"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/276675.276685"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref41","first-page":"2743","article-title":"VILLAIN: Backdoor attacks against vertical split learning","volume-title":"Proc. 32nd USENIX Secur. Symp.","author":"Bai"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/sp46215.2023.10179446"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2022.3208630"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3572795"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2025.3593115"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3159831"},{"key":"ref47","first-page":"11138","article-title":"Can graph neural networks learn language with extremely weak text supervision?","volume-title":"Proc. 63rd Annu. Meeting Assoc. Comput. Linguistics (Long Papers)","author":"Li"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0787"},{"key":"ref49","article-title":"Graph attention networks","author":"Velickovic","year":"2017","journal-title":"arXiv:1710.10903"},{"key":"ref50","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. 5th Int. Conf. Learn. Represent. (ICLR)","author":"Kipf"},{"key":"ref51","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. NIPS","author":"Hamilton"},{"key":"ref52","article-title":"How powerful are graph neural networks?","volume-title":"Proc. 7th Int. Conf. Learn. Represent.","author":"Xu"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2022.3144250"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29304"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref57","first-page":"16978","article-title":"Structure learning of latent factors via clique search on correlation thresholded graphs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kim"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2325"},{"key":"ref59","first-page":"14747","article-title":"Deep leakage from gradients","volume-title":"Proc. 33rd Conf. Neural Inf. Process. Syst.","author":"Zhu"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.129476"},{"key":"ref62","article-title":"Vertically federated graph neural network for privacy-preserving node classification","author":"Chen","year":"2020","journal-title":"arXiv:2005.11903"},{"key":"ref63","article-title":"FedGNN: Federated graph neural network for privacy-preserving recommendation","author":"Wu","year":"2021","journal-title":"arXiv:2102.04925"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2023.3326359"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.463"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/11313711\/11523557.pdf?arnumber=11523557","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T19:34:13Z","timestamp":1780342453000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11523557\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":65,"URL":"https:\/\/doi.org\/10.1109\/tifs.2026.3694646","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}