{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:28:33Z","timestamp":1778347713390,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679759","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:21Z","timestamp":1729452861000},"page":"1316-1325","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Privacy-Preserving Graph Embedding based on Local Differential Privacy"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1364-0166","authenticated-orcid":false,"given":"Zening","family":"Li","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8658-6599","authenticated-orcid":false,"given":"Rong-Hua","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5808-3131","authenticated-orcid":false,"given":"Meihao","family":"Liao","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0609-8833","authenticated-orcid":false,"given":"Fusheng","family":"Jin","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0181-8379","authenticated-orcid":false,"given":"Guoren","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Aleksandar Bojchevski Johannes Klicpera Bryan Perozzi Amol Kapoor Martin Blais Benedek R\u00f3zemberczki Michal Lukasik and Stephan G\u00fcnnemann. 2020. Scaling graph neural networks with approximate pagerank. In KDD. 2464--2473.","DOI":"10.1145\/3394486.3403296"},{"key":"e_1_3_2_1_2_1","volume-title":"Gaurav Aggarwal, and Prateek Jain.","author":"Daigavane Ameya","year":"2021","unstructured":"Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, and Prateek Jain. 2021. Node-level differentially private graph neural networks. arXiv preprint arXiv:2111.15521 (2021)."},{"key":"e_1_3_2_1_3_1","unstructured":"Wei-Yen Day Ninghui Li and Min Lyu. 2016. Publishing graph degree distribution with node differential privacy. In SIGMOD. 123--138."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Laxman Dhulipala Quanquan C Liu Sofya Raskhodnikova Jessica Shi Julian Shun and Shangdi Yu. 2022. Differential privacy from locally adjustable graph algorithms: k-core decomposition low out-degree ordering and densest subgraphs. In FOCS. 754--765.","DOI":"10.1109\/FOCS54457.2022.00077"},{"key":"e_1_3_2_1_5_1","unstructured":"Bolin Ding Janardhan Kulkarni and Sergey Yekhanin. 2017. Collecting telemetry data privately. In NeurIPS. 3571--3580."},{"key":"e_1_3_2_1_6_1","volume-title":"Differentially private triangle counting in large graphs. TKDE","author":"Ding Xiaofeng","year":"2021","unstructured":"Xiaofeng Ding, Shujun Sheng, Huajian Zhou, Xiaodong Zhang, Zhifeng Bao, Pan Zhou, and Hai Jin. 2021. Differentially private triangle counting in large graphs. TKDE (2021)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"John C Duchi Michael I Jordan and Martin J Wainwright. 2013. Local privacy and statistical minimax rates. In FOCS. 429--438.","DOI":"10.1109\/FOCS.2013.53"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1389735"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Cynthia Dwork Frank McSherry Kobbi Nissim and Adam Smith. 2006. Calibrating noise to sensitivity in private data analysis. In TCC. 265--284.","DOI":"10.1007\/11681878_14"},{"key":"e_1_3_2_1_10_1","volume-title":"Differentially private graph learning via sensitivity-bounded personalized pagerank. NeurIPS","author":"Epasto Alessandro","year":"2022","unstructured":"Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, and Peilin Zhong. 2022. Differentially private graph learning via sensitivity-bounded personalized pagerank. NeurIPS (2022), 22617--22627."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660348"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Anupam Gupta Katrina Ligett Frank McSherry Aaron Roth and Kunal Talwar. 2010. Differentially private combinatorial optimization. In SODA. 1106--1125.","DOI":"10.1137\/1.9781611973075.90"},{"key":"e_1_3_2_1_13_1","unstructured":"Jacob Imola Takao Murakami and Kamalika Chaudhuri. 2021. Locally differentially private analysis of graph statistics. In USENIX Security. 983--1000."},{"key":"e_1_3_2_1_14_1","unstructured":"Jacob Imola Takao Murakami and Kamalika Chaudhuri. 2022. Communication-efficient triangle counting under local differential privacy. In USENIX Security. 537--554."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Hongwei Jin and Xun Chen. 2022. Gromov-wasserstein discrepancy with local differential privacy for distributed structural graphs. In IJCAI. 2115--2121.","DOI":"10.24963\/ijcai.2022\/294"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Zach Jorgensen Ting Yu and Graham Cormode. 2016. Publishing attributed social graphs with formal privacy guarantees. In SIGMOD. 107--122.","DOI":"10.1145\/2882903.2915215"},{"key":"e_1_3_2_1_17_1","first-page":"1146","article-title":"Private analysis of graph structure","volume":"4","author":"Karwa Vishesh","year":"2011","unstructured":"Vishesh Karwa, Sofya Raskhodnikova, Adam Smith, and Grigory Yaroslavtsev. 2011. Private analysis of graph structure. VLDB, Vol. 4, 11 (2011), 1146--1157.","journal-title":"VLDB"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1137\/090756090"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Shiva Prasad Kasiviswanathan Kobbi Nissim Sofya Raskhodnikova and Adam Smith. 2013. Analyzing graphs with node differential privacy. In TCC. 457--476.","DOI":"10.1007\/978-3-642-36594-2_26"},{"key":"e_1_3_2_1_20_1","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_1_21_1","unstructured":"Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Predict then propagate: Graph neural networks meet personalized pagerank. In ICLR."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Aashish Kolluri Teodora Baluta Bryan Hooi and Prateek Saxena. 2022. LPGNet: Link private graph networks for node classification. In CCS. 1813--1827.","DOI":"10.1145\/3548606.3560705"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Zitao Li Tianhao Wang Milan Lopuha\u00e4-Zwakenberg Ninghui Li and Boris vSkoric. 2020. Estimating numerical distributions under local differential privacy. In SIGMOD. 621--635.","DOI":"10.1145\/3411497.3420215"},{"key":"e_1_3_2_1_24_1","first-page":"2936","article-title":"Towards private learning on decentralized graphs with local differential privacy","volume":"17","author":"Lin Wanyu","year":"2022","unstructured":"Wanyu Lin, Baochun Li, and Cong Wang. 2022. Towards private learning on decentralized graphs with local differential privacy. TIFS, Vol. 17 (2022), 2936--2946.","journal-title":"TIFS"},{"key":"e_1_3_2_1_25_1","volume-title":"Path-aware siamese graph neural network for link prediction. arXiv preprint arXiv:2208.05781","author":"Lv Jingsong","year":"2022","unstructured":"Jingsong Lv, Zhao Li, Hongyang Chen, Yao Qi, and Chunqi Wu. 2022. Path-aware siamese graph neural network for link prediction. arXiv preprint arXiv:2208.05781 (2022)."},{"key":"e_1_3_2_1_26_1","unstructured":"Microsoft. 2021. Neural Network Intelligence. https:\/\/github.com\/microsoft\/nni"},{"key":"e_1_3_2_1_27_1","unstructured":"Dung Nguyen and Anil Vullikanti. 2021. Differentially private densest subgraph detection. In ICML. 8140--8151."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Kobbi Nissim Sofya Raskhodnikova and Adam Smith. 2007. Smooth sensitivity and sampling in private data analysis. In STOC. 75--84.","DOI":"10.1145\/1250790.1250803"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Zhan Qin Ting Yu Yin Yang Issa Khalil Xiaokui Xiao and Kui Ren. 2017. Generating synthetic decentralized social graphs with local differential privacy. In CCS. 425--438.","DOI":"10.1145\/3133956.3134086"},{"key":"e_1_3_2_1_30_1","volume-title":"Netsmf: Large-scale network embedding as sparse matrix factorization. In WWW. 1509--1520.","author":"Qiu Jiezhong","year":"2019","unstructured":"Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang. 2019. Netsmf: Large-scale network embedding as sparse matrix factorization. In WWW. 1509--1520."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Sina Sajadmanesh and Daniel Gatica-Perez. 2021. Locally private graph neural networks. In CCS. 2130--2145.","DOI":"10.1145\/3460120.3484565"},{"key":"e_1_3_2_1_33_1","volume-title":"Aur\u00e9lien Bellet, and Daniel Gatica-Perez.","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 USENIX Security."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Haipei Sun Xiaokui Xiao Issa Khalil Yin Yang Zhan Qin Hui Wang and Ting Yu. 2019. Analyzing subgraph statistics from extended local views with decentralized differential privacy. In CCS. 703--717.","DOI":"10.1145\/3319535.3354253"},{"key":"e_1_3_2_1_35_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Velivckovi\u0107 Petar","year":"2017","unstructured":"Petar Velivckovi\u0107, 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_1_36_1","doi-asserted-by":"crossref","unstructured":"Hanzhi Wang Mingguo He Zhewei Wei Sibo Wang Ye Yuan Xiaoyong Du and Ji-Rong Wen. 2021. Approximate graph propagation. In KDD. 1686--1696.","DOI":"10.1145\/3447548.3467243"},{"key":"e_1_3_2_1_37_1","volume-title":"Hyejin Shin, Junbum Shin, and Ge Yu.","author":"Wang Ning","year":"2019","unstructured":"Ning Wang, Xiaokui Xiao, Yin Yang, Jun Zhao, Siu Cheung Hui, Hyejin Shin, Junbum Shin, and Ge Yu. 2019. Collecting and analyzing multidimensional data with local differential privacy. In ICDE. 638--649."},{"key":"e_1_3_2_1_38_1","volume-title":"Pairwise learning for neural link prediction. arXiv preprint arXiv:2112.02936","author":"Wang Zhitao","year":"2021","unstructured":"Zhitao Wang, Yong Zhou, Litao Hong, Yuanhang Zou, and Hanjing Su. 2021. Pairwise learning for neural link prediction. arXiv preprint arXiv:2112.02936 (2021)."},{"key":"e_1_3_2_1_39_1","volume-title":"DPNE: Differentially private network embedding. In PAKDD. 235--246.","author":"Xu Depeng","year":"2018","unstructured":"Depeng Xu, Shuhan Yuan, Xintao Wu, and HaiNhat Phan. 2018. DPNE: Differentially private network embedding. In PAKDD. 235--246."},{"key":"e_1_3_2_1_40_1","volume-title":"Xiaofeng Meng, and Xiaokui Xiao.","author":"Ye Qingqing","year":"2020","unstructured":"Qingqing Ye, Haibo Hu, Man Ho Au, Xiaofeng Meng, and Xiaokui Xiao. 2020. Towards locally differentially private generic graph metric estimation. In ICDE. 1922--1925."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Yuan Yin and Zhewei Wei. 2019. Scalable graph embeddings via sparse transpose proximities. In KDD. 1429--1437.","DOI":"10.1145\/3292500.3330860"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Jun Zhang Graham Cormode Cecilia M Procopiuc Divesh Srivastava and Xiaokui Xiao. 2015. Private release of graph statistics using ladder functions. In SIGMOD. 731--745.","DOI":"10.1145\/2723372.2737785"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2927365"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Shijie Zhang Hongzhi Yin Tong Chen Zi Huang Lizhen Cui and Xiangliang Zhang. 2021. Graph embedding for recommendation against attribute inference attacks. In WWW. 3002--3014.","DOI":"10.1145\/3442381.3449813"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Xingyi Zhang Kun Xie Sibo Wang and Zengfeng Huang. 2021. Learning based proximity matrix factorization for node embedding. In KDD. 2243--2253.","DOI":"10.1145\/3447548.3467296"}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","location":"Boise ID USA","acronym":"CIKM '24","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679759","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679759","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:28Z","timestamp":1750294708000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":45,"alternative-id":["10.1145\/3627673.3679759","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679759","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}