{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T03:05:09Z","timestamp":1763348709965,"version":"3.45.0"},"reference-count":29,"publisher":"Tech Science Press","issue":"2","license":[{"start":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T00:00:00Z","timestamp":1740268800000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2024.057814","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T02:23:26Z","timestamp":1734402206000},"page":"1857-1877","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["PIAFGNN: Property Inference Attacks against Federated Graph Neural Networks"],"prefix":"10.32604","volume":"82","author":[{"given":"Jiewen","family":"Liu","sequence":"first","affiliation":[]},{"given":"Baolu","family":"Xue","sequence":"additional","affiliation":[]},{"given":"Mengya","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yuntao","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Chen","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3339474","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang","year":"Jan. 2019","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref2","series-title":"Proc. 20th Int. Conf. Artif. Intell. Stat.","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"54","author":"McMahan","year":"Apr. 20\u201322, 2017"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1109\/TII.2021.3085960","article-title":"Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in iiot","volume":"18","author":"Jia","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref4","first-page":"1","article-title":"Federated graph neural networks: Overview, techniques, and challenges","author":"Liu","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref5","series-title":"Proc. 28th ACM SIGKDD Conf. Knowl. Discov. Data Min.","first-page":"4110","article-title":"Federatedscope-GNN: Towards a unified, comprehensive and efficient package for federated graph learning","author":"Wang","year":"2022"},{"key":"ref6","series-title":"Proc. 38th Annual Comput. Secur. Appl. Conf.","first-page":"684","article-title":"More is better (mostly): On the backdoor attacks in federated graph neural networks","author":"Xu","year":"2022"},{"key":"ref7","unstructured":"C. He et al., \u201cFedGraphNN: A federated learning system and benchmark for graph neural networks,\u201d 2021. doi: 10.48550\/arXiv.2104.07145."},{"key":"ref8","doi-asserted-by":"crossref","first-page":"3091","DOI":"10.1038\/s41467-022-30714-9","article-title":"A federated graph neural network framework for privacy-preserving personalization","volume":"13","author":"Wu","year":"2022","journal-title":"Nat. Commun."},{"key":"ref9","unstructured":"C. Chen et al., \u201cVertically federated graph neural network for privacy-preserving node classification,\u201d 2020. doi: 10.48550\/arXiv.2005.11903."},{"key":"ref10","series-title":"31st USENIX Secur. Symp. (USENIX Secur. 22)","first-page":"4543","article-title":"Inference attacks against graph neural networks","author":"Zhang","year":"2022"},{"key":"ref11","series-title":"Proc. 2022 ACM SIGSAC Conf. Comput. Commun. Secur.","first-page":"2871","article-title":"Group property inference attacks against graph neural networks","author":"Wang","year":"2022"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"75729","DOI":"10.1109\/ACCESS.2022.3191784","article-title":"A comprehensive survey of graph neural networks for knowledge graphs","volume":"10","author":"Ye","year":"2022","journal-title":"IEEE Access"},{"key":"ref13","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126441","article-title":"A survey of graph neural network based recommendation in social networks","volume":"549","author":"Li","year":"2023, Art. no. 126441","journal-title":"Neurocomputing"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1093\/bib\/bbac408","article-title":"FP-GNN: A versatile deep learning architecture for enhanced molecular property prediction","volume":"23","author":"Cai","year":"Sep. 2022","journal-title":"Brief. Bioinform."},{"key":"ref15","series-title":"ICC, 2020-2020 IEEE Int. Conf. Commun. (ICC)","first-page":"1","article-title":"Gan enhanced membership inference: A passive local attack in federated learning","author":"Zhang","year":"2020"},{"key":"ref16","series-title":"2019 IEEE Symp. Secur. Priv. (SP)","first-page":"691","article-title":"Exploiting unintended feature leakage in collaborative learning","author":"Melis","year":"2019"},{"key":"ref17","series-title":"2017 IEEE Symp. Secur. Priv. (SP)","first-page":"3","article-title":"Membership inference attacks against machine learning models","author":"Shokri","year":"2017"},{"key":"ref18","series-title":"Proc. 15th ACM Workshop Artif. Intell. Secur., AISec\u201922","first-page":"1","article-title":"Label-only membership inference attack against node-level graph neural networks","author":"Conti","year":"2022"},{"key":"ref19","unstructured":"T. N. Kipf and M. Welling, \u201cSemi-supervised classification with graph convolutional networks,\u201d 2016. doi: 10.48550\/arXiv.1609.02907."},{"key":"ref20","series-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst., NIPS\u201917","first-page":"1025","article-title":"Inductive representation learning on large graphs","author":"Hamilton","year":"2017"},{"key":"ref21","unstructured":"P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio and Y. Bengio, \u201cGraph attention networks,\u201d vol. 1050, no. 20, pp. 10\u201348, 550, 2017. doi: 10.48550\/arXiv.1710.10903."},{"key":"ref22","series-title":"2021 IEEE 37th Int. Conf. Data Eng. (ICDE)","first-page":"181","article-title":"Feature inference attack on model predictions in vertical federated learning","author":"Luo","year":"2021"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TCSS.2022.3161016","article-title":"Graph-Fraudster: Adversarial attacks on graph neural network-based vertical federated learning","volume":"10","author":"Chen","year":"2023","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref24","series-title":"Proc. 2018 ACM SIGSAC Conf. Comput. Commun. Secur., CCS \u201918","first-page":"619","article-title":"Property inference attacks on fully connected neural networks using permutation invariant representations","author":"Ganju","year":"2018"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1023\/A:1009953814988","article-title":"Automating the construction of internet portals with machine learning","volume":"3","author":"McCallum","year":"2000","journal-title":"Inf. Retr."},{"key":"ref26","series-title":"Proc. Third ACM Conf. Digital Libraries","first-page":"89","article-title":"CiteSeer: An automatic citation indexing system","author":"Giles","year":"1998"},{"key":"ref27","article-title":"Collective classification in network data","volume":"29","author":"Sen","year":"Sep. 2008","journal-title":"AI Mag."},{"key":"ref28","series-title":"30th USENIX Secur. Symp. (USENIX Secur. 21)","first-page":"929","article-title":"PrivSyn: Differentially private data synthesis","author":"Zhang","year":"Aug. 2021"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends Mach. Learn."}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-82-2\/TSP_CMC_57814\/TSP_CMC_57814.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:09:46Z","timestamp":1763100586000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v82n2\/59454"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":29,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.057814","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2024-08-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-28","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-17","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}