{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:23:09Z","timestamp":1764782589902,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","funder":[{"name":"National Key R&D Program of China","award":["2022YFB3103700, 2022YFB3103704"],"award-info":[{"award-number":["2022YFB3103700, 2022YFB3103704"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDB0680201"],"award-info":[{"award-number":["XDB0680201"]}]},{"name":"Beijing Natural Science Foundation","award":["4252023"],"award-info":[{"award-number":["4252023"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,7]]},"DOI":"10.1145\/3767695.3769497","type":"proceedings-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:14:58Z","timestamp":1764782098000},"page":"75-84","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Data Usage Privacy Auditing for Semi-supervised GNNs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0241-1976","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1197-5212","authenticated-orcid":false,"given":"Kaike","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8319-2681","authenticated-orcid":false,"given":"Bingbing","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6146-148X","authenticated-orcid":false,"given":"Fei","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3454-4789","authenticated-orcid":false,"given":"Qi","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1081-8119","authenticated-orcid":false,"given":"Huawei","family":"Shen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology, CAS, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,6]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Dikaios: Privacy auditing of algorithmic fairness via attribute inference attacks. arXiv preprint arXiv:2202.02242","author":"Aalmoes Jan","year":"2022","unstructured":"Jan Aalmoes, Vasisht Duddu, and Antoine Boutet. 2022. Dikaios: Privacy auditing of algorithmic fairness via attribute inference attacks. arXiv preprint arXiv:2202.02242 (2022)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.21552\/EDPL\/2016\/3\/4"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341161.3342958"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833649"},{"key":"e_1_3_2_1_5_1","volume-title":"Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247","author":"Chen Jie","year":"2018","unstructured":"Jie Chen, Tengfei Ma, and Cao Xiao. 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3569452"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3560830.3563734"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448891.3448939"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3501396"},{"key":"e_1_3_2_1_10_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_11_1","volume-title":"Graph neural networks with continual learning for fake news detection from social media. arXiv preprint arXiv:2007.03316","author":"Han Yi","year":"2020","unstructured":"Yi Han, Shanika Karunasekera, and Christopher Leckie. 2020. Graph neural networks with continual learning for fake news detection from social media. arXiv preprint arXiv:2007.03316 (2020)."},{"key":"e_1_3_2_1_12_1","volume-title":"USENIX Security Symposium. 2669-2686","author":"He Xinlei","year":"2021","unstructured":"Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang. 2021a. Stealing Links from Graph Neural Networks. In USENIX Security Symposium. 2669-2686."},{"key":"e_1_3_2_1_13_1","first-page":"365","volume-title":"Tel Aviv","author":"He Xinlei","year":"2022","unstructured":"Xinlei He, Hongbin Liu, Neil Zhenqiang Gong, and Yang Zhang. 2022. Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXI. Springer, 365-381."},{"key":"e_1_3_2_1_14_1","volume-title":"Node-level membership inference attacks against graph neural networks. arXiv preprint arXiv:2102.05429","author":"He Xinlei","year":"2021","unstructured":"Xinlei He, Rui Wen, Yixin Wu, Michael Backes, Yun Shen, and Yang Zhang. 2021b. Node-level membership inference attacks against graph neural networks. arXiv preprint arXiv:2102.05429 (2021)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3523273"},{"key":"e_1_3_2_1_16_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422337.3447836"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3559359"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3484575"},{"key":"e_1_3_2_1_20_1","volume-title":"Large-margin softmax loss for convolutional neural networks. arXiv preprint arXiv:1612.02295","author":"Liu Weiyang","year":"2016","unstructured":"Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang. 2016. Large-margin softmax loss for convolutional neural networks. arXiv preprint arXiv:1612.02295 (2016)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75762-5_57"},{"key":"e_1_3_2_1_22_1","volume-title":"Understanding membership inferences on well-generalized learning models. arXiv preprint arXiv:1802.04889","author":"Long Yunhui","year":"2018","unstructured":"Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen. 2018. Understanding membership inferences on well-generalized learning models. arXiv preprint arXiv:1802.04889 (2018)."},{"key":"e_1_3_2_1_23_1","volume-title":"Quantifying privacy risks of masked language models using membership inference attacks. arXiv preprint arXiv:2203.03929","author":"Mireshghallah Fatemehsadat","year":"2022","unstructured":"Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, Taylor Berg-Kirkpatrick, and Reza Shokri. 2022. Quantifying privacy risks of masked language models using membership inference attacks. arXiv preprint arXiv:2203.03929 (2022)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243855"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPSISA52974.2021.00002"},{"key":"e_1_3_2_1_27_1","volume-title":"A survey of privacy attacks in machine learning. arXiv preprint arXiv:2007.07646","author":"Rigaki Maria","year":"2020","unstructured":"Maria Rigaki and Sebastian Garcia. 2020. A survey of privacy attacks in machine learning. arXiv preprint arXiv:2007.07646 (2020)."},{"key":"e_1_3_2_1_28_1","volume-title":"Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models. arXiv preprint arXiv:1806.01246","author":"Salem Ahmed","year":"2018","unstructured":"Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. 2018. Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models. arXiv preprint arXiv:1806.01246 (2018)."},{"key":"e_1_3_2_1_29_1","volume-title":"Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868","author":"Shchur Oleksandr","year":"2018","unstructured":"Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 (2018)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3000372"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1353-4858(16)30056-3"},{"key":"e_1_3_2_1_33_1","volume-title":"Attention-based graph neural network for semi-supervised learning. arXiv preprint arXiv:1803.03735","author":"Thekumparampil Kiran K","year":"2018","unstructured":"Kiran K Thekumparampil, Chong Wang, Sewoong Oh, and Li-Jia Li. 2018. Attention-based graph neural network for semi-supervised learning. arXiv preprint arXiv:1803.03735 (2018)."},{"key":"e_1_3_2_1_34_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2014.2320500"},{"key":"e_1_3_2_1_36_1","volume-title":"Graph attention networks. stat","author":"Velickovic Petar","year":"2017","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. stat, Vol. 1050 (2017), 20."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17203"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"e_1_3_2_1_39_1","first-page":"19839","article-title":"Graph stochastic neural networks for semi-supervised learning","volume":"33","author":"Wang Haibo","year":"2020","unstructured":"Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, and Jilong Wang. 2020. Graph stochastic neural networks for semi-supervised learning. Advances in Neural Information Processing Systems, Vol. 33 (2020), 19839-19848.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_40_1","volume-title":"A review on graph neural network methods in financial applications. arXiv preprint arXiv:2111.15367","author":"Wang Jianian","year":"2021","unstructured":"Jianian Wang, Sheng Zhang, Yanghua Xiao, and Rui Song. 2021b. A review on graph neural network methods in financial applications. arXiv preprint arXiv:2111.15367 (2021)."},{"key":"e_1_3_2_1_41_1","volume-title":"Membership inference attacks on knowledge graphs. arXiv preprint arXiv:2104.08273","author":"Wang Yu","year":"2021","unstructured":"Yu Wang, Lifu Huang, Philip S Yu, and Lichao Sun. 2021a. Membership inference attacks on knowledge graphs. arXiv preprint arXiv:2104.08273 (2021)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539392"},{"key":"e_1_3_2_1_43_1","volume-title":"A classification of location privacy attacks and approaches. Personal and ubiquitous computing","author":"Wernke Marius","year":"2014","unstructured":"Marius Wernke, Pavel Skvortsov, Frank D\u00fcrr, and Kurt Rothermel. 2014. A classification of location privacy attacks and approaches. Personal and ubiquitous computing, Vol. 18 (2014), 163-175."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00182"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833806"},{"key":"e_1_3_2_1_46_1","volume-title":"International conference on machine learning. PMLR, 6861-6871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International conference on machine learning. PMLR, 6861-6871."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494523"},{"key":"e_1_3_2_1_48_1","volume-title":"Revisiting Semi-Supervised Learning with Graph Embeddings. CoRR abs\/1603.08861","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. 2016. Revisiting Semi-Supervised Learning with Graph Embeddings. CoRR abs\/1603.08861 (2016). arXiv:1603.08861 http:\/\/arxiv.org\/abs\/1603.08861"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560675"},{"key":"e_1_3_2_1_50_1","first-page":"268","article-title":"Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st computer security foundations symposium (CSF)","author":"Yeom Samuel","year":"2018","unstructured":"Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018. Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st computer security foundations symposium (CSF). IEEE, 268-282.","journal-title":"IEEE"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599319"}],"event":{"name":"SIGIR-AP 2025:Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region","location":"Xi'an China","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region"],"original-title":[],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:16:01Z","timestamp":1764782161000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3767695.3769497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,6]]},"references-count":51,"alternative-id":["10.1145\/3767695.3769497","10.1145\/3767695"],"URL":"https:\/\/doi.org\/10.1145\/3767695.3769497","relation":{},"subject":[],"published":{"date-parts":[[2025,12,6]]},"assertion":[{"value":"2025-12-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}