{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:02:47Z","timestamp":1773414167094,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key R&D Program of China","award":["2019YFE0198200"],"award-info":[{"award-number":["2019YFE0198200"]}]},{"name":"GRF","award":["16211520,16205322"],"award-info":[{"award-number":["16211520,16205322"]}]},{"name":"RIF","award":["R6020-19,R6021-20"],"award-info":[{"award-number":["R6020-19,R6021-20"]}]},{"name":"UGC Research Matching Grants","award":["RMGS20EG01-D,RMGS20CR11,RMGS20CR12,RMGS20EG19,RMGS20EG21,RMGS23CR05,RMGS23EG08"],"award-info":[{"award-number":["RMGS20EG01-D,RMGS20CR11,RMGS20CR12,RMGS20EG19,RMGS20EG21,RMGS23CR05,RMGS23EG08"]}]},{"name":"NSFC","award":["U20B2053"],"award-info":[{"award-number":["U20B2053"]}]},{"name":"MHKJFS","award":["MHP\/001\/19"],"award-info":[{"award-number":["MHP\/001\/19"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3614933","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:26Z","timestamp":1697874326000},"page":"823-832","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Independent Distribution Regularization for Private Graph Embedding"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4499-7805","authenticated-orcid":false,"given":"Qi","family":"Hu","sequence":"first","affiliation":[{"name":"HKUST, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7818-6090","authenticated-orcid":false,"given":"Yangqiu","family":"Song","sequence":"additional","affiliation":[{"name":"HKUST, Hong Kong, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Chirag Agarwal Himabindu Lakkaraju and Marinka Zitnik. 2021. Towards a unified framework for fair and stable graph representation learning. In Uncertainty in Artificial Intelligence. PMLR 2114--2124.  Chirag Agarwal Himabindu Lakkaraju and Marinka Zitnik. 2021. Towards a unified framework for fair and stable graph representation learning. In Uncertainty in Artificial Intelligence. PMLR 2114--2124."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2488388.2488393"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482215"},{"key":"e_1_3_2_1_4_1","volume-title":"Advances in Neural Information Processing Systems","volume":"14","author":"Belkin Mikhail","year":"2001","unstructured":"Mikhail Belkin and Partha Niyogi . 2001 . Laplacian eigenmaps and spectral techniques for embedding and clustering . Advances in Neural Information Processing Systems , Vol. 14 (2001). Mikhail Belkin and Partha Niyogi. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems, Vol. 14 (2001)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1017\/ATSIP.2020.13"},{"key":"e_1_3_2_1_6_1","volume-title":"A tutorial on network embeddings. arXiv preprint arXiv:1808.02590","author":"Chen Haochen","year":"2018","unstructured":"Haochen Chen , Bryan Perozzi , Rami Al-Rfou , and Steven Skiena . 2018. A tutorial on network embeddings. arXiv preprint arXiv:1808.02590 ( 2018 ). Haochen Chen, Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2018. A tutorial on network embeddings. arXiv preprint arXiv:1808.02590 (2018)."},{"key":"e_1_3_2_1_7_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). 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_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2926745"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Vasisht Duddu Antoine Boutet and Virat Shejwalkar. 2020. Quantifying privacy leakage in graph embedding. In MobiQuitous 2020--17th EAI International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services. 76--85.  Vasisht Duddu Antoine Boutet and Virat Shejwalkar. 2020. Quantifying privacy leakage in graph embedding. In MobiQuitous 2020--17th EAI International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services. 76--85.","DOI":"10.1145\/3448891.3448939"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3154793"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_12_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 a. Inductive representation learning on large graphs . Advances in Neural Information Processing Systems , Vol. 30 (2017). Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017a. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_13_1","volume-title":"Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584","author":"Hamilton William L","year":"2017","unstructured":"William L Hamilton , Rex Ying , and Jure Leskovec . 2017b. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 ( 2017 ). William L Hamilton, Rex Ying, and Jure Leskovec. 2017b. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 (2017)."},{"key":"e_1_3_2_1_14_1","volume-title":"Fedgraphnn: A federated learning system and benchmark for graph neural networks. arXiv preprint arXiv:2104.07145","author":"He Chaoyang","year":"2021","unstructured":"Chaoyang He , Keshav Balasubramanian , Emir Ceyani , Carl Yang , Han Xie , Lichao Sun , Lifang He , Liangwei Yang , Philip S Yu , Yu Rong , 2021 a. Fedgraphnn: A federated learning system and benchmark for graph neural networks. arXiv preprint arXiv:2104.07145 (2021). Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S Yu, Yu Rong, et al. 2021a. Fedgraphnn: A federated learning system and benchmark for graph neural networks. arXiv preprint arXiv:2104.07145 (2021)."},{"key":"e_1_3_2_1_15_1","volume-title":"30th USENIX Security Symposium (USENIX Security 21)","author":"He Xinlei","year":"2021","unstructured":"Xinlei He , Jinyuan Jia , Michael Backes , Neil Zhenqiang Gong , and Yang Zhang . 2021 b. Stealing links from graph neural networks . In 30th USENIX Security Symposium (USENIX Security 21) . 2669--2686. Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang. 2021b. Stealing links from graph neural networks. In 30th USENIX Security Symposium (USENIX Security 21). 2669--2686."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511975"},{"key":"e_1_3_2_1_17_1","volume-title":"27th USENIX Security Symposium (USENIX Security 18)","author":"Jia Jinyuan","year":"2018","unstructured":"Jinyuan Jia and Neil Zhenqiang Gong . 2018 . $$AttriGuard$$: A practical defense against attribute inference attacks via adversarial machine learning . In 27th USENIX Security Symposium (USENIX Security 18) . 513--529. Jinyuan Jia and Neil Zhenqiang Gong. 2018. $$AttriGuard$$: A practical defense against attribute inference attacks via adversarial machine learning. In 27th USENIX Security Symposium (USENIX Security 18). 513--529."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-36594-2_26"},{"key":"e_1_3_2_1_20_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_1_21_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 . 2016a. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016a. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_22_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016b. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 ( 2016 ). Thomas N Kipf and Max Welling. 2016b. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3036583"},{"key":"e_1_3_2_1_24_1","unstructured":"Peiyuan Liao Han Zhao Keyulu Xu Tommi S Jaakkola Geoff Gordon Stefanie Jegelka and Ruslan Salakhutdinov. 2020. Graph adversarial networks: Protecting information against adversarial attacks. (2020).  Peiyuan Liao Han Zhao Keyulu Xu Tommi S Jaakkola Geoff Gordon Stefanie Jegelka and Ruslan Salakhutdinov. 2020. Graph adversarial networks: Protecting information against adversarial attacks. (2020)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539302"},{"key":"e_1_3_2_1_26_1","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Moyer Daniel","year":"2018","unstructured":"Daniel Moyer , Shuyang Gao , Rob Brekelmans , Aram Galstyan , and Greg Ver Steeg . 2018 . Invariant representations without adversarial training . Advances in Neural Information Processing Systems , Vol. 31 (2018). Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, and Greg Ver Steeg. 2018. Invariant representations without adversarial training. Advances in Neural Information Processing Systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539232"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPSISA52974.2021.00002"},{"key":"e_1_3_2_1_29_1","volume-title":"Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407","author":"Pan Shirui","year":"2018","unstructured":"Shirui Pan , Ruiqi Hu , Guodong Long , Jing Jiang , Lina Yao , and Chengqi Zhang . 2018. Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407 ( 2018 ). Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. 2018. Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407 (2018)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482252"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_32_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). 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_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487601"},{"key":"e_1_3_2_1_34_1","volume-title":"25th USENIX security symposium (USENIX Security 16). 601--618.","author":"Tram\u00e8r Florian","unstructured":"Florian Tram\u00e8r , Fan Zhang , Ari Juels , Michael K Reiter , and Thomas Ristenpart . 2016. Stealing machine learning models via prediction $$APIs$$ . In 25th USENIX security symposium (USENIX Security 16). 601--618. Florian Tram\u00e8r, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart. 2016. Stealing machine learning models via prediction $$APIs$$. In 25th USENIX security symposium (USENIX Security 16). 601--618."},{"key":"e_1_3_2_1_35_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Petar Velivc","year":"2017","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Velivc kovi\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","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio etal 2017. Graph attention networks. stat Vol. 1050 20 (2017) 10--48550.  Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio et al. 2017. Graph attention networks. stat Vol. 1050 20 (2017) 10--48550."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467273"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"e_1_3_2_1_39_1","volume-title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 ( 2018 ). Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2021.9010015"},{"key":"e_1_3_2_1_41_1","volume-title":"Proceedings of the 31th USENIX Security Symposium. 1--18","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 Proceedings of the 31th USENIX Security Symposium. 1--18 . Zhikun Zhang, Min Chen, Michael Backes, Yun Shen, and Yang Zhang. 2022. Inference attacks against graph neural networks. In Proceedings of the 31th USENIX Security Symposium. 1--18."},{"key":"e_1_3_2_1_42_1","volume-title":"Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131","author":"Zhu Yanqiao","year":"2020","unstructured":"Yanqiao Zhu , Yichen Xu , Feng Yu , Qiang Liu , Shu Wu , and Liang Wang . 2020. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 ( 2020 ). Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 (2020)."}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614933","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3614933","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:44Z","timestamp":1750178204000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614933"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":42,"alternative-id":["10.1145\/3583780.3614933","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3614933","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}