{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T17:18:47Z","timestamp":1790961527484,"version":"4.1.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031696503","type":"print"},{"value":"9783031696510","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-69651-0_24","type":"book-chapter","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T07:30:40Z","timestamp":1726126240000},"page":"358-373","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Node Injection Link Stealing Attack"],"prefix":"10.1007","author":[{"given":"Oualid","family":"Zari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Parra-Arnau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ay\u015fe","family":"\u00dcnsal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melek","family":"\u00d6nen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"24_CR1","unstructured":"Atwood, J., Towsley, D.: Diffusion-convolutional neural networks. In: 30th International Conference on Neural Information Processing Systems (NIPS) (2016)"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Brunet, S., Canard, S., Gambs, S., Olivier, B.: Novel differentially private mechanisms for graphs. IACR Cryptology ePrint Archive, p.\u00a0745 (2016)","DOI":"10.1109\/PST.2016.7906935"},{"key":"24_CR3","unstructured":"Cadwalladr, C., Graham-Harrison, E.: Revealed: 50 million Facebook profiles harvested for Cambridge analytica in major data breach. The Guardian (2018)"},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Duddu, V., Boutet, A., Shejwalkar, V.: Quantifying privacy leakage in graph embedding. In: 17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) (2021)","DOI":"10.1145\/3448891.3448939"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Dwork, C.: Differential privacy. In: 33rd International Colloquium on Automata, Languages and Programming, ICALP (2006)","DOI":"10.1007\/11787006_1"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Theory of Cryptography (2006)","DOI":"10.1007\/11681878_14"},{"issue":"3\u20134","key":"24_CR7","first-page":"211","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3\u20134), 211\u2013407 (2014)","journal-title":"Found. Trends Theor. Comput. Sci."},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Hay, M., Li, C., Miklau, G., Jensen, D.: Accurate estimation of the degree distribution of private networks. In: IEEE International Conference on Data Mining (2009)","DOI":"10.1109\/ICDM.2009.11"},{"key":"24_CR9","unstructured":"He, X., Jia, J., Backes, M., Gong, N.Z., Zhang, Y.: Stealing links from graph neural networks. In: USENIX Security Symposium (2021)"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Karwa, V., Slavkovic, A.B.: Differentially private graphical degree sequences and synthetic graphs. In: Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD (2012)","DOI":"10.1007\/978-3-642-33627-0_21"},{"key":"24_CR11","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations (ICLR) (2017)"},{"key":"24_CR12","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations (ICLR) (2017)"},{"issue":"5757","key":"24_CR13","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1126\/science.1116869","volume":"311","author":"G Kossinets","year":"2006","unstructured":"Kossinets, G., Watts, D.J.: Empirical analysis of an evolving social network. Science 311(5757), 88\u201390 (2006)","journal-title":"Science"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Li, K., et al.: Towards practical edge inference attacks against graph neural networks. In: 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 (2023)","DOI":"10.1109\/ICASSP49357.2023.10096675"},{"key":"24_CR15","unstructured":"Mueller, T.T., Usynin, D., Paetzold, J.C., Rueckert, D., Kaissis, G.: SoK: differential privacy on graph-structured data. CoRR abs\/2203.09205 (2022)"},{"key":"24_CR16","unstructured":"Rozemberczki, B., Sarkar, R.: Twitch gamers: a dataset for evaluating proximity preserving and structural role-based node embeddings. arXiv preprint arXiv:2101.03091 (2021)"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Sajadmanesh, S., Shamsabadi, A.S., Bellet, A., Gatica-Perez, D.: GAP: differentially private graph neural networks with aggregation perturbation. CoRR abs\/2203.00949 (2022)","DOI":"10.1145\/3460120.3484565"},{"issue":"1","key":"24_CR18","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2009)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"3","key":"24_CR19","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93 (2008)","journal-title":"AI Mag."},{"key":"24_CR20","unstructured":"Sun, L., et al.: Adversarial attack and defense on graph data: a survey. arXiv preprint arXiv:1812.10528 (2018)"},{"key":"24_CR21","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: 6th International Conference on Learning Representations (ICLR) (2018)"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Wu, B., Yang, X., Pan, S., Yuan, X.: Adapting membership inference attacks to GNN for graph classification: approaches and implications. In: IEEE International Conference on Data Mining (ICDM) (2021)","DOI":"10.1109\/ICDM51629.2021.00182"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Wu, F., Long, Y., Zhang, C., Li, B.: Linkteller: recovering private edges from graph neural networks via influence analysis. In: 2022 IEEE Symposium on Security and Privacy (SP) (2022)","DOI":"10.1109\/SP46214.2022.9833806"},{"key":"24_CR24","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: 7th International Conference on Learning Representations (ICLR) (2019)"},{"key":"24_CR25","unstructured":"Zeng, H., Zhou, H., Srivastava, A., Kannan, R., Prasanna, V.K.: Graphsaint: graph sampling based inductive learning method. In: 8th International Conference on Learning Representations (ICLR) (2020)"},{"key":"24_CR26","unstructured":"Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"24_CR27","doi-asserted-by":"crossref","unstructured":"Zhu, K., Fioretto, F., Van\u00a0Hentenryck, P.: Post-processing of differentially private data: a fairness perspective. In: 31st International Joint Conference on Artificial Intelligence, IJCAI (2022)","DOI":"10.24963\/ijcai.2022\/559"}],"container-title":["Lecture Notes in Computer Science","Privacy in Statistical Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-69651-0_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T01:16:00Z","timestamp":1732756560000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-69651-0_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031696503","9783031696510"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-69651-0_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"13 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PSD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Privacy in Statistical Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Antibes Juan-les-Pins","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"psd2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/crises-deim.urv.cat\/psd2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}