{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:47:15Z","timestamp":1777873635848,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100006374","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3303302"],"award-info":[{"award-number":["2022YFC3303302"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2436209, 62476263, 62406307"],"award-info":[{"award-number":["U2436209, 62476263, 62406307"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"Beijing Nova Program","doi-asserted-by":"publisher","award":["20230484430"],"award-info":[{"award-number":["20230484430"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Funding of ICT, CAS","award":["E461060"],"award-info":[{"award-number":["E461060"]}]},{"DOI":"10.13039\/501100006374","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023M743567"],"award-info":[{"award-number":["2023M743567"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Fellowship Program of CPSF","award":["GZB20240761"],"award-info":[{"award-number":["GZB20240761"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3736992","type":"proceedings-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T13:32:14Z","timestamp":1754055134000},"page":"767-777","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["GRASP: Differentially Private Graph Reconstruction Defense with Structured Perturbation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4932-6429","authenticated-orcid":false,"given":"Zhiyu","family":"Guo","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1525-0788","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Key Lab of AI Safety, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9633-8361","authenticated-orcid":false,"given":"Xiang","family":"Ao","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8833-5398","authenticated-orcid":false,"given":"Qing","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_2_2_1","volume-title":"International Conference on Machine Learning. PMLR, 1473-1483","author":"Chanpuriya Sudhanshu","year":"2021","unstructured":"Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, and Charalampos Tsourakakis. 2021. Deepwalking backwards: From embeddings back to graphs. In International Conference on Machine Learning. PMLR, 1473-1483."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1966"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714665"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448891.3448939"},{"key":"e_1_3_2_2_6_1","volume-title":"International Colloquium on Automata, Languages, and Programming","author":"Dwork Cynthia","unstructured":"Cynthia Dwork. 2006. Differential privacy. In International Colloquium on Automata, Languages, and Programming. Springer, 1-12."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10656-4"},{"key":"e_1_3_2_2_9_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems, Vol. 30."},{"key":"e_1_3_2_2_10_1","volume-title":"Neil Zhenqiang Gong, and Yang Zhang","author":"He Xinlei","year":"2021","unstructured":"Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang. 2021. Stealing links from graph neural networks. In 30th USENIX security symposium. 2669-2686."},{"key":"e_1_3_2_2_11_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. 2021. Node-level membership inference attacks against graph neural networks. arXiv preprint arXiv:2102.05429 (2021)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3523273"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512178"},{"key":"e_1_3_2_2_14_1","volume-title":"International Conference on Learning Representations.","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and MaxWelling. 2017. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560705"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422337.3447836"},{"key":"e_1_3_2_2_17_1","volume-title":"International Conference on Learning Representations.","author":"Li Kuan","year":"2024","unstructured":"Kuan Li, YiWen Chen, Yang Liu, Jin Wang, Qing He, Minhao Cheng, and Xiang Ao. 2024. Boosting the adversarial robustness of graph neural networks: An OOD perspective. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539484"},{"key":"e_1_3_2_2_19_1","volume-title":"International Conference on Learning Representations.","author":"Li Kuan","year":"2023","unstructured":"Kuan Li, Yang Liu, Xiang Ao, and Qing He. 2023. Revisiting graph adversarial attack and defense from a data distribution perspective. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539483"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599355"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449989"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3098"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2007.41"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3576915.3623173"},{"key":"e_1_3_2_2_26_1","volume-title":"Wiki-cs: A wikipedia-based benchmark for graph neural networks. arXiv preprint arXiv:2007.02901","author":"Mernyei P\u00e9ter","year":"2020","unstructured":"P\u00e9ter Mernyei and Catalina Cangea. 2020. Wiki-cs: A wikipedia-based benchmark for graph neural networks. arXiv preprint arXiv:2007.02901 (2020)."},{"key":"e_1_3_2_2_27_1","volume-title":"2017 IEEE 30th Computer Security Foundations Symposium. IEEE, 263-275","author":"Mironov Ilya","year":"2017","unstructured":"Ilya Mironov. 2017. R\u00e9nyi differential privacy. In 2017 IEEE 30th Computer Security Foundations Symposium. IEEE, 263-275."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPSISA52974.2021.00002"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_30_1","volume-title":"32nd USENIX Security Symposium. 3223-3240","author":"Sajadmanesh Sina","year":"2023","unstructured":"Sina Sajadmanesh, Ali Shahin Shamsabadi, Aur\u00e9lien Bellet, and Daniel Gatica- Perez. 2023. GAP: Differentially private graph neural networks with aggregation perturbation. In 32nd USENIX Security Symposium. 3223-3240."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"e_1_3_2_2_32_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 Gunnemann. 2018. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 (2018)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"e_1_3_2_2_34_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Velickovic Petar","year":"2017","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_2_35_1","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1667-1676","author":"Wang Binghui","year":"2021","unstructured":"Binghui Wang, Jiayi Guo, Ang Li, Yiran Chen, and Hai Li. 2021. Privacypreserving representation learning on graphs: A mutual information perspective. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1667-1676."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833806"},{"key":"e_1_3_2_2_37_1","first-page":"90891","article-title":"On provable privacy vulnerabilities of graph representations","volume":"37","author":"Wu Ruofan","year":"2024","unstructured":"Ruofan Wu, Guanhua Fang, Mingyang Zhang, Qiying Pan, Tengfei Liu, and Weiqiang Wang. 2024. On provable privacy vulnerabilities of graph representations. In Advances in Neural Information Processing Systems, Vol. 37. 90891-90933.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_38_1","volume-title":"NeurIPS 2023 Workshop: New Frontiers in Graph Learning.","author":"Wu Ruofan","year":"2023","unstructured":"Ruofan Wu, Mingyang Zhang, Lingjuan Lyu, Xiaolong Xu, Xiuquan Hao, Xinyi Fu, Tengfei Liu, Tianyi Zhang, and Weiqiang Wang. 2023. Privacy-preserving design of graph neural networks with applications to vertical federated learning. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning."},{"key":"e_1_3_2_2_39_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Xu Jingjing","year":"2019","unstructured":"Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, and Junyang Lin. 2019. Understanding and improving layer normalization. In Advances in Neural Information Processing Systems, Vol. 32."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i12.33433"},{"key":"e_1_3_2_2_41_1","volume-title":"31st USENIX Security Symposium. 4543-4560","author":"Zhang Zhikun","year":"2022","unstructured":"Zhikun Zhang, Min Chen, Michael Backes, Yun Shen, and Yang Zhang. 2022. Inference attacks against graph neural networks. In 31st USENIX Security Symposium. 4543-4560."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3207915"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/516"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3490237"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.5555\/3618408.3620215"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3736992","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:07:48Z","timestamp":1777572468000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3736992"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":45,"alternative-id":["10.1145\/3711896.3736992","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3736992","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}