{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T10:44:10Z","timestamp":1764585850150,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,13]]},"DOI":"10.1145\/3733800.3763263","type":"proceedings-article","created":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T10:39:10Z","timestamp":1764585550000},"page":"39-47","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SPG: Ensuring Structural Privacy in Secure Graph Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9058-0560","authenticated-orcid":false,"given":"Yiming","family":"Qin","sequence":"first","affiliation":[{"name":"Monash University, Clayton, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0374-3593","authenticated-orcid":false,"given":"Shangqi","family":"Lai","sequence":"additional","affiliation":[{"name":"CSIRO's Data61, Clayton, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6656-6240","authenticated-orcid":false,"given":"Joseph","family":"Liu","sequence":"additional","affiliation":[{"name":"Monash University, Clayton, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0547-315X","authenticated-orcid":false,"given":"Cong","family":"Wang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3701-4946","authenticated-orcid":false,"given":"Xingliang","family":"Yuan","sequence":"additional","affiliation":[{"name":"The University of Melbourne, Melbourne, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_3_1_3_2","first-page":"509","volume-title":"Applied Cryptography and Network Security: 17th International Conference","author":"Aly Abdelrahaman","year":"2019","unstructured":"Abdelrahaman Aly and Nigel\u00a0P. Smart. 2019. Benchmarking Privacy Preserving Scientific Operations. In Applied Cryptography and Network Security: 17th International Conference. Springer-Verlag, Berlin, Heidelberg, 509\u2013529."},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978331"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978429"},{"key":"e_1_3_3_1_6_2","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"Chien Eli","year":"2023","unstructured":"Eli Chien, Wei-Ning Chen, Chao Pan, Pan Li, Ayfer \u00d6zg\u00fcr, and Olgica Milenkovic. 2023. Differentially private decoupled graph convolutions for multigranular topology protection. In Proceedings of the 37th International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 1966, 21\u00a0pages."},{"key":"e_1_3_3_1_7_2","unstructured":"Ameya Daigavane Gagan Madan Aditya Sinha Abhradeep\u00a0Guha Thakurta Gaurav Aggarwal and Prateek Jain. 2021. Node-Level Differentially Private Graph Neural Networks. CoRR abs\/2111.15521 (2021). arXiv:https:\/\/arXiv.org\/abs\/2111.15521"},{"key":"e_1_3_3_1_8_2","unstructured":"Dfns. 2025. https:\/\/www.dfns.co\/. Accessed: 2025-06-06."},{"key":"e_1_3_3_1_9_2","first-page":"76","volume-title":"MobiQuitous 2020 - 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services","author":"Duddu Vasisht","year":"2021","unstructured":"Vasisht Duddu, Antoine Boutet, and Virat Shejwalkar. 2021. Quantifying Privacy Leakage in Graph Embedding. In MobiQuitous 2020 - 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. Association for Computing Machinery, New York, NY, USA, 76\u201385."},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_3_3_1_12_2","first-page":"2669","volume-title":"30th USENIX Security Symposium","author":"He Xinlei","year":"2021","unstructured":"Xinlei He, Jinyuan Jia, Michael Backes, Neil\u00a0Zhenqiang Gong, and Yang Zhang. 2021. Stealing Links from Graph Neural Networks. In 30th USENIX Security Symposium. USENIX Association, Berkeley, CA, USA, 2669\u20132686."},{"key":"e_1_3_3_1_13_2","unstructured":"Seira Hidano and Takao Murakami. 2024. Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy. Trans. Data Priv. 17 2 (2024) 89\u2013121."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417872"},{"key":"e_1_3_3_1_15_2","series-title":"Proceedings of Machine Learning Research","first-page":"10912","volume-title":"Proceedings of the 39th International Conference on Machine Learning","volume":"162","author":"Keller Marcel","year":"2022","unstructured":"Marcel Keller and Ke Sun. 2022. Secure Quantized Training for Deep Learning. In Proceedings of the 39th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a0162), Kamalika Chaudhuri, Stefanie Jegelka, Le\u00a0Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (Eds.). PMLR, 10912\u201310938."},{"key":"e_1_3_3_1_16_2","volume-title":"5th International Conference on Learning Representations","author":"Kipf Thomas\u00a0N.","year":"2017","unstructured":"Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations. OpenReview.net, Toulon, France."},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1145\/3548606.3560705","volume-title":"Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security","author":"Kolluri Aashish","year":"2022","unstructured":"Aashish Kolluri, Teodora Baluta, Bryan Hooi, and Prateek Saxena. 2022. LPGNet: Link Private Graph Networks for Node Classification. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery, New York, NY, USA, 1813\u20131827."},{"key":"e_1_3_3_1_18_2","first-page":"507","volume-title":"Proceedings of The 33rd International Conference on Machine Learning","volume":"48","author":"Liu Weiyang","year":"2016","unstructured":"Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang. 2016. Large-Margin Softmax Loss for Convolutional Neural Networks. In Proceedings of The 33rd International Conference on Machine Learning, Maria\u00a0Florina Balcan and Kilian\u00a0Q. Weinberger (Eds.), Vol.\u00a048. PMLR, New York, New York, USA, 507\u2013516."},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599479"},{"key":"e_1_3_3_1_20_2","first-page":"52","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13\u201316, 2021, Proceedings, Part III","author":"Ma Zuanjie","year":"2021","unstructured":"Zuanjie Ma, Hongming Gu, and Zhenhua Liu. 2021. Understanding Drug Abuse Social Network Using Weighted Graph Neural Networks Explainer. In Computational Science and Its Applications \u2013 ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13\u201316, 2021, Proceedings, Part III. Springer-Verlag, Berlin, Heidelberg, 52\u201361."},{"key":"e_1_3_3_1_21_2","unstructured":"Adil\u00a0Mudasir Malla and Asif\u00a0Ali Banka. 2023. A Systematic Review of Deep Graph Neural Networks: Challenges Classification Architectures Applications & Potential Utility in Bioinformatics. CoRR abs\/2311.02127 (2023)."},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Miller McPherson Lynn Smith-Lovin and James\u00a0M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology 27 1 (2001) 415\u2013444.","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104425"},{"key":"e_1_3_3_1_24_2","volume-title":"Wild and interesting Facebook statistics and facts (2021)","author":"Osman Maddy","year":"2021","unstructured":"Maddy Osman. 2021. Wild and interesting Facebook statistics and facts (2021). https:\/\/kinsta.com\/blog\/facebook-statistics\/"},{"key":"e_1_3_3_1_25_2","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"Peng Hongwu","year":"2023","unstructured":"Hongwu Peng, Ran Ran, Yukui Luo, Jiahui Zhao, Shaoyi Huang, Kiran Thorat, Tong Geng, Chenghong Wang, Xiaolin Xu, Wujie Wen, and Caiwen Ding. 2023. LinGCN: structural linearized graph convolutional network for homomorphically encrypted inference. In Proceedings of the 37th International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 908, 16\u00a0pages."},{"key":"e_1_3_3_1_26_2","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"Ran Ran","year":"2023","unstructured":"Ran Ran, Nuo Xu, Tao Liu, Wei Wang, Gang Quan, and Wujie Wen. 2023. Penguin: parallel-packed homomorphic encryption for fast graph convolutional network inference. In Proceedings of the 37th International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 837, 13\u00a0pages."},{"key":"e_1_3_3_1_27_2","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","author":"Ran Ran","year":"2022","unstructured":"Ran Ran, Nuo Xu, Wei Wang, Gang Quan, Jieming Yin, and Wujie Wen. 2022. CryptoGCN: fast and scalable homomorphically encrypted graph convolutional network inference. In Proceedings of the 36th International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 2731, 14\u00a0pages."},{"key":"e_1_3_3_1_28_2","volume-title":"Proceedings of the 32nd USENIX Conference on Security Symposium","author":"Sajadmanesh Sina","year":"2023","unstructured":"Sina Sajadmanesh, Ali\u00a0Shahin Shamsabadi, Aur\u00e9lien Bellet, and Daniel Gatica-Perez. 2023. GAP: differentially private graph neural networks with aggregation perturbation. In Proceedings of the 32nd USENIX Conference on Security Symposium. USENIX Association, USA, Article 181, 18\u00a0pages."},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"crossref","unstructured":"Songlei Wang Yifeng Zheng and Xiaohua Jia. 2023. SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service. IEEE Transactions on Services Computing 16 04 (July 2023) 2923\u20132938.","DOI":"10.1109\/TSC.2023.3241615"},{"key":"e_1_3_3_1_30_2","first-page":"2005","volume-title":"43rd IEEE Symposium on Security and Privacy","author":"Wu Fan","year":"2022","unstructured":"Fan Wu, Yunhui Long, Ce Zhang, and Bo Li. 2022. LINKTELLER: Recovering Private Edges from Graph Neural Networks via Influence Analysis. In 43rd IEEE Symposium on Security and Privacy. IEEE, San Francisco, CA, USA, 2005\u20132024."},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"crossref","first-page":"4714","DOI":"10.1109\/SP54263.2024.00270","volume-title":"2024 IEEE Symposium on Security and Privacy","author":"Xiang Zihang","year":"2024","unstructured":"Zihang Xiang, Tianhao Wang, and Di Wang. 2024. Preserving Node-level Privacy in Graph Neural Networks. In 2024 IEEE Symposium on Security and Privacy. IEEE Computer Society, Los Alamitos, CA, USA, 4714\u20134732."},{"key":"e_1_3_3_1_32_2","first-page":"2209","volume-title":"33rd USENIX Security Symposium","author":"Xu Zhibo","year":"2024","unstructured":"Zhibo Xu, Shangqi Lai, Xiaoning Liu, Alsharif Abuadbba, Xingliang Yuan, and Xun Yi. 2024. OblivGNN: Oblivious Inference on Transductive and Inductive Graph Neural Network. In 33rd USENIX Security Symposium. USENIX Association, Philadelphia, PA, 2209\u20132226."},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3555041.3589398"},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3658644.3670300"}],"event":{"name":"LAMPS '25: Proceedings of the 2025 Workshop on Large AI Systems and Models with Privacy and Security Analysis","location":"Taipei Taiwan","acronym":"LAMPS '25","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"]},"container-title":["Proceedings of the 2025 Workshop on Large AI Systems and Models with Privacy and Security Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3733800.3763263","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T10:39:27Z","timestamp":1764585567000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3733800.3763263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,13]]},"references-count":33,"alternative-id":["10.1145\/3733800.3763263","10.1145\/3733800"],"URL":"https:\/\/doi.org\/10.1145\/3733800.3763263","relation":{},"subject":[],"published":{"date-parts":[[2025,10,13]]},"assertion":[{"value":"2025-12-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}