{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T20:37:32Z","timestamp":1770323852581,"version":"3.49.0"},"reference-count":103,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T00:00:00Z","timestamp":1667520000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T00:00:00Z","timestamp":1667520000000},"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":["The VLDB Journal"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00778-022-00768-8","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T12:36:04Z","timestamp":1667565364000},"page":"717-736","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["P$$^2$$CG: a privacy preserving collaborative graph neural network training framework"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9371-8358","authenticated-orcid":false,"given":"Xupeng","family":"Miao","sequence":"first","affiliation":[]},{"given":"Wentao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yuezihan","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Fangcheng","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Yingxia","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yangyu","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Cui","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"key":"768_CR1","unstructured":"Tencent Inc. Weshi (2021). https:\/\/weishi.qq.com"},{"key":"768_CR2","unstructured":"Abu-El-Haija, S., Kapoor, A., Perozzi, B., Lee, J.: N-GCN: multi-scale graph convolution for semi-supervised node classification. In: UAI, p. 310 (2019)"},{"key":"768_CR3","unstructured":"Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., Steeg, G.V., Galstyan, A.: Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9\u201315 June 2019, Long Beach, California, USA, Proceedings of Machine Learning Research, vol.\u00a097, pp. 21\u201329. PMLR (2019). http:\/\/proceedings.mlr.press\/v97\/abu-el-haija19a.html"},{"key":"768_CR4","doi-asserted-by":"publisher","first-page":"287","DOI":"10.21552\/EDPL\/2016\/3\/4","volume":"2","author":"JP Albrecht","year":"2016","unstructured":"Albrecht, J.P.: How the GDPR will change the world. Eur. Data Prot. L. Rev. 2, 287 (2016)","journal-title":"Eur. Data Prot. L. Rev."},{"key":"768_CR5","doi-asserted-by":"publisher","unstructured":"Aono, Y., Hayashi, T., Phong, L.T., Wang, L.: Scalable and secure logistic regression via homomorphic encryption. In: CODASPY, pp. 142\u2013144 (2016). https:\/\/doi.org\/10.1145\/2857705.2857731","DOI":"10.1145\/2857705.2857731"},{"key":"768_CR6","doi-asserted-by":"publisher","unstructured":"Beigi, G., Mosallanezhad, A., Guo, R., Alvari, H., Nou, A., Liu, H.: Privacy-aware recommendation with private-attribute protection using adversarial learning. In: WSDM, pp. 34\u201342 (2020). https:\/\/doi.org\/10.1145\/3336191.3371832","DOI":"10.1145\/3336191.3371832"},{"key":"768_CR7","doi-asserted-by":"publisher","unstructured":"Bonchi, F., Gionis, A., Tassa, T.: Identity obfuscation in graphs through the information theoretic lens. In: ICDE (2011). https:\/\/doi.org\/10.1109\/ICDE.2011.5767905","DOI":"10.1109\/ICDE.2011.5767905"},{"key":"768_CR8","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. In: ICLR (2014). arXiv:http:\/\/arxiv.org\/abs\/1312.6203"},{"key":"768_CR9","unstructured":"Chaudhuri, K., Monteleoni, C.: Privacy-preserving logistic regression. In: NIPS, pp. 289\u2013296 (2008). http:\/\/papers.nips.cc\/paper\/3486-privacy-preserving-logistic-regression"},{"key":"768_CR10","unstructured":"Chen, M., Wei, Z., Ding, B., Li, Y., Yuan, Y., Du, X., Wen, J.: Scalable graph neural networks via bidirectional propagation. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6\u201312, 2020, Virtual (2020). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/a7789ef88d599b8df86bbee632b2994d-Abstract.html"},{"key":"768_CR11","doi-asserted-by":"publisher","unstructured":"Chen, X., Song, X., Ren, R., Zhu, L., Cheng, Z., Nie, L.: Fine-grained privacy detection with graph-regularized hierarchical attentive representation learning. TOIS 38(4), 37:1\u201337:26 (2020). https:\/\/doi.org\/10.1145\/3406109","DOI":"10.1145\/3406109"},{"key":"768_CR12","unstructured":"Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., Yang, Q.: Secureboost: a lossless federated learning framework. CoRR. arXiv:1901.08755 (2019)"},{"key":"768_CR13","unstructured":"Cristofaro, E.D., Tsudik, G.: Practical private set intersection protocols with linear computational and bandwidth complexity. IACR Cryptol. ePrint Arch. 2009, 491 (2009). http:\/\/eprint.iacr.org\/2009\/491"},{"key":"768_CR14","unstructured":"Dankar, F.K.: Privacy preserving linear regression on distributed databases. Trans. Data Priv. 8(1), 3\u201328 (2015). http:\/\/www.tdp.cat\/issues11\/tdp.a215a15.pdf"},{"key":"768_CR15","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: NeurIPS, pp. 3837\u20133845 (2016)"},{"key":"768_CR16","doi-asserted-by":"publisher","unstructured":"Dev, H.: Privacy preserving social graphs for high precision community detection. In: SIGMOD, pp. 1615\u20131616 (2014). https:\/\/doi.org\/10.1145\/2588555.2612668","DOI":"10.1145\/2588555.2612668"},{"key":"768_CR17","doi-asserted-by":"publisher","unstructured":"Dong, C., Chen, L., Wen, Z.: When private set intersection meets big data: an efficient and scalable protocol. In: Sadeghi, A., Gligor, V.D., Yung, M. (eds.) 2013 ACM SIGSAC Conference on Computer and Communications Security, CCS\u201913, Berlin, Germany, November 4\u20138, 2013, pp. 789\u2013800. ACM (2013). https:\/\/doi.org\/10.1145\/2508859.2516701","DOI":"10.1145\/2508859.2516701"},{"key":"768_CR18","doi-asserted-by":"crossref","unstructured":"Duong, C.T., Hoang, D., Yin, H., Weidlich, M., Nguyen, Q.V.H., Aberer, K.: Efficient streaming subgraph isomorphism with graph neural networks. Proc. VLDB Endow. 14(5), 730\u2013742 (2021). http:\/\/www.vldb.org\/pvldb\/vol14\/p730-duong.pdf","DOI":"10.14778\/3446095.3446097"},{"key":"768_CR19","doi-asserted-by":"publisher","unstructured":"Dwork, C.: Differential privacy: A survey of results. In: TAMC, pp. 1\u201319 (2008). https:\/\/doi.org\/10.1007\/978-3-540-79228-4_1","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"768_CR20","doi-asserted-by":"publisher","unstructured":"Dwork, C., McSherry, F., Nissim, K., Smith, A.D.: Calibrating noise to sensitivity in private data analysis. In: TCC (2006). https:\/\/doi.org\/10.1007\/11681878_14","DOI":"10.1007\/11681878_14"},{"key":"768_CR21","doi-asserted-by":"publisher","unstructured":"Eli\u00e1s, M., Kapralov, M., Kulkarni, J., Lee, Y.T.: Differentially private release of synthetic graphs. In: SODA, pp. 560\u2013578 (2020). https:\/\/doi.org\/10.1137\/1.9781611975994.34","DOI":"10.1137\/1.9781611975994.34"},{"key":"768_CR22","doi-asserted-by":"publisher","unstructured":"Freedman, M.J., Nissim, K., Pinkas, B.: Efficient private matching and set intersection. In: Cachin, C., Camenisch, J. (eds.) Advances in Cryptology\u2014EUROCRYPT 2004, International Conference on the Theory and Applications of Cryptographic Techniques, Interlaken, Switzerland, May 2\u20136, 2004, Proceedings, Lecture Notes in Computer Science, vol. 3027, pp. 1\u201319. Springer (2004). https:\/\/doi.org\/10.1007\/978-3-540-24676-3_1","DOI":"10.1007\/978-3-540-24676-3_1"},{"key":"768_CR23","doi-asserted-by":"publisher","unstructured":"Friedman, A., Schuster, A.: Data mining with differential privacy. In: SIGKDD, pp. 493\u2013502 (2010). https:\/\/doi.org\/10.1145\/1835804.1835868","DOI":"10.1145\/1835804.1835868"},{"key":"768_CR24","doi-asserted-by":"publisher","unstructured":"Fu, H., Zhang, A., Xie, X.: Effective social graph deanonymization based on graph structure and descriptive information. TIST 6(4), 49:1\u201349:29 (2015). https:\/\/doi.org\/10.1145\/2700836","DOI":"10.1145\/2700836"},{"key":"768_CR25","doi-asserted-by":"crossref","unstructured":"Gao, J., Chen, J., Li, Z., Zhang, J.: ICS-GNN: lightweight interactive community search via graph neural network. Proc. VLDB Endow. 14(6), 1006\u20131018 (2021). http:\/\/www.vldb.org\/pvldb\/vol14\/p1006-gao.pdf","DOI":"10.14778\/3447689.3447704"},{"key":"768_CR26","doi-asserted-by":"publisher","unstructured":"Gao, T., Li, F.: Sharing social networks using a novel differentially private graph model. In: CCNC, pp. 1\u20134 (2019). https:\/\/doi.org\/10.1109\/CCNC.2019.8651689","DOI":"10.1109\/CCNC.2019.8651689"},{"key":"768_CR27","unstructured":"Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K.E., Naehrig, M., Wernsing, J.: Cryptonets: applying neural networks to encrypted data with high throughput and accuracy. In: ICML (2016)"},{"key":"768_CR28","doi-asserted-by":"publisher","unstructured":"Gong, M., Pan, K., Xie, Y., Qin, A.K., Tang, Z.: Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition. Neural Netw. (2020). https:\/\/doi.org\/10.1016\/j.neunet.2020.02.001","DOI":"10.1016\/j.neunet.2020.02.001"},{"key":"768_CR29","unstructured":"Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NeurIPS, pp. 1024\u20131034 (2017)"},{"key":"768_CR30","doi-asserted-by":"crossref","unstructured":"Hanai, M., Suzumura, T., Tan, W.J., Liu, E.S., Theodoropoulos, G., Cai, W.: Distributed edge partitioning for trillion-edge graphs. PVLDB 12(13), 2379\u20132392 (2019). http:\/\/www.vldb.org\/pvldb\/vol12\/p2379-hanai.pdf","DOI":"10.14778\/3358701.3358706"},{"key":"768_CR31","doi-asserted-by":"publisher","unstructured":"Harth, A., Umbrich, J., Hogan, A., Decker, S.: YARS2: a federated repository for querying graph structured data from the web. In: The Semantic Web, 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, ISWC 2007 + ASWC 2007, Busan, Korea, November 11\u201315, 2007, Lecture Notes in Computer Science, vol. 4825, pp. 211\u2013224. Springer (2007). https:\/\/doi.org\/10.1007\/978-3-540-76298-0_16","DOI":"10.1007\/978-3-540-76298-0_16"},{"key":"768_CR32","doi-asserted-by":"publisher","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: simplifying and powering graph convolution network for recommendation. In: SIGIR, pp. 639\u2013648 (2020). https:\/\/doi.org\/10.1145\/3397271.3401063","DOI":"10.1145\/3397271.3401063"},{"key":"768_CR33","doi-asserted-by":"publisher","unstructured":"Hu, Y., Niu, D., Yang, J., Zhou, S.: FDML: A collaborative machine learning framework for distributed features. In: SIGKDD (2019). https:\/\/doi.org\/10.1145\/3292500.3330765","DOI":"10.1145\/3292500.3330765"},{"key":"768_CR34","doi-asserted-by":"publisher","unstructured":"Huang, J., Abadi, D.: LEOPARD: lightweight edge-oriented partitioning and replication for dynamic graphs. PVLDB 9(7), 540\u2013551 (2016). https:\/\/doi.org\/10.14778\/2904483.2904486. http:\/\/www.vldb.org\/pvldb\/vol9\/p540-huang.pdf","DOI":"10.14778\/2904483.2904486"},{"issue":"11","key":"768_CR35","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.14778\/3402707.3402747","volume":"4","author":"J Huang","year":"2011","unstructured":"Huang, J., Abadi, D.J., Ren, K.: Scalable SPARQL querying of large RDF graphs. Proc. VLDB Endow. 4(11), 1123\u20131134 (2011)","journal-title":"Proc. VLDB Endow."},{"key":"768_CR36","doi-asserted-by":"publisher","unstructured":"Jin, B., Gao, C., He, X., Jin, D., Li, Y.: Multi-behavior recommendation with graph convolutional networks. In: SIGIR, pp. 659\u2013668 (2020). https:\/\/doi.org\/10.1145\/3397271.3401072","DOI":"10.1145\/3397271.3401072"},{"key":"768_CR37","doi-asserted-by":"publisher","unstructured":"Jorgensen, Z., Yu, T., Cormode, G.: Publishing attributed social graphs with formal privacy guarantees. In: SIGMOD (2016). https:\/\/doi.org\/10.1145\/2882903.2915215","DOI":"10.1145\/2882903.2915215"},{"issue":"1","key":"768_CR38","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1137\/S1064827595287997","volume":"20","author":"G Karypis","year":"1998","unstructured":"Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM 20(1), 359\u2013392 (1998). https:\/\/doi.org\/10.1137\/S1064827595287997","journal-title":"SIAM"},{"key":"768_CR39","doi-asserted-by":"publisher","unstructured":"Khan, K., ur\u00a0Rehman, S., Aziz, K., Fong, S., Sarasvady, S., Vishwa, A.: DBSCAN: past, present and future. In: The 5th International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2014, Chennai, India, February 17\u201319, 2014, pp. 232\u2013238. IEEE (2014). https:\/\/doi.org\/10.1109\/ICADIWT.2014.6814687","DOI":"10.1109\/ICADIWT.2014.6814687"},{"key":"768_CR40","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)"},{"issue":"8","key":"768_CR41","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009). https:\/\/doi.org\/10.1109\/MC.2009.263","journal-title":"Computer"},{"key":"768_CR42","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)"},{"key":"768_CR43","doi-asserted-by":"publisher","unstructured":"Lai, S., Yuan, X., Sun, S., Liu, J.K., Liu, Y., Liu, D.: Graphse$${^2}$$: An encrypted graph database for privacy-preserving social search. In: Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security, AsiaCCS 2019, Auckland, New Zealand, July 09\u201312, 2019, pp. 41\u201354. ACM (2019). https:\/\/doi.org\/10.1145\/3321705.3329803","DOI":"10.1145\/3321705.3329803"},{"issue":"4","key":"768_CR44","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989). https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput."},{"key":"768_CR45","doi-asserted-by":"publisher","unstructured":"Li, K., Lu, G., Luo, G., Cai, Z.: Seed-free graph de-anonymiztiation with adversarial learning. In: CIKM, pp. 745\u2013754 (2020). https:\/\/doi.org\/10.1145\/3340531.3411970","DOI":"10.1145\/3340531.3411970"},{"key":"768_CR46","doi-asserted-by":"publisher","unstructured":"Lieberman, M.D., Choudhury, S., Hughes, M., Patrone, D., Jr., R.T.H., Piatko, C.D., Chapman, M., Marple, J.P., Silberberg, D.: Parasol: An architecture for cross-cloud federated graph querying. In: Proceedings of the 3rd Workshop on Data analytics in the Cloud, DanaC 2014, June 22, 2014, Snowbird, Utah, USA, In Conjunction with ACM SIGMOD\/PODS Conference, pp. 4:1\u20134:4. ACM (2014). https:\/\/doi.org\/10.1145\/2627770.2627771","DOI":"10.1145\/2627770.2627771"},{"key":"768_CR47","unstructured":"Lin, Y., Han, S., Mao, H., Wang, Y., Dally, B.: Deep gradient compression: reducing the communication bandwidth for distributed training. In: ICLR (2018)"},{"issue":"18","key":"768_CR48","doi-asserted-by":"publisher","first-page":"3864","DOI":"10.1002\/sec.1306","volume":"8","author":"C Liu","year":"2015","unstructured":"Liu, C., Liu, I., Yao, W., Li, J.: K-Anonymity against neighborhood attacks in weighted social networks. Secur Commun Netw 8(18), 3864\u20133882 (2015). https:\/\/doi.org\/10.1002\/sec.1306","journal-title":"Secur Commun Netw"},{"key":"768_CR49","doi-asserted-by":"publisher","unstructured":"Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: SIGMOD, pp. 93\u2013106 (2008). https:\/\/doi.org\/10.1145\/1376616.1376629","DOI":"10.1145\/1376616.1376629"},{"key":"768_CR50","doi-asserted-by":"publisher","unstructured":"Liu, S., Ounis, I., Macdonald, C., Meng, Z.: A heterogeneous graph neural model for cold-start recommendation. In: SIGIR, pp. 2029\u20132032 (2020). https:\/\/doi.org\/10.1145\/3397271.3401252","DOI":"10.1145\/3397271.3401252"},{"key":"768_CR51","doi-asserted-by":"publisher","unstructured":"Mao, K., Xiao, X., Zhu, J., Lu, B., Tang, R., He, X.: Item tagging for information retrieval: a tripartite graph neural network based approach. In: SIGIR, pp. 2327\u20132336 (2020). https:\/\/doi.org\/10.1145\/3397271.3401438","DOI":"10.1145\/3397271.3401438"},{"key":"768_CR52","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: AISTATS, vol.\u00a054, pp. 1273\u20131282 (2017)"},{"key":"768_CR53","unstructured":"McMahan, H.B., Moore, E., Ramage, D., y\u00a0Arcas, B.A.: Federated learning of deep networks using model averaging. CoRR (2016). arXiv:http:\/\/arxiv.org\/abs\/1602.05629"},{"key":"768_CR54","doi-asserted-by":"publisher","unstructured":"McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: FOCS, pp. 94\u2013103 (2007). https:\/\/doi.org\/10.1109\/FOCS.2007.41","DOI":"10.1109\/FOCS.2007.41"},{"key":"768_CR55","unstructured":"Mittal, P., Papamanthou, C., Song, D.X.: Preserving link privacy in social network based systems. In: NDSS (2013)"},{"key":"768_CR56","doi-asserted-by":"publisher","unstructured":"Mohassel, P., Zhang, Y.: Secureml: a system for scalable privacy-preserving machine learning. In: SP, pp. 19\u201338 (2017). https:\/\/doi.org\/10.1109\/SP.2017.12","DOI":"10.1109\/SP.2017.12"},{"key":"768_CR57","doi-asserted-by":"publisher","unstructured":"Monti, F., Boscaini, D., Masci, J., Rodol\u00e0, E., Svoboda, J., Bronstein, M.M.: Geometric deep learning on graphs and manifolds using mixture model CNNs. In: CVPR, pp. 5425\u20135434 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.576","DOI":"10.1109\/CVPR.2017.576"},{"issue":"1","key":"768_CR58","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1002\/widm.53","volume":"2","author":"F Murtagh","year":"2012","unstructured":"Murtagh, F., Contreras, P.: Algorithms for hierarchical clustering: an overview. WIREs Data Mining Knowl. Discov. 2(1), 86\u201397 (2012). https:\/\/doi.org\/10.1002\/widm.53","journal-title":"WIREs Data Mining Knowl. Discov."},{"key":"768_CR59","unstructured":"Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: T.G. Dietterich, S.\u00a0Becker, Z.\u00a0Ghahramani (eds.) Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, BC, Canada], pp. 849\u2013856. MIT Press (2001). https:\/\/proceedings.neurips.cc\/paper\/2001\/hash\/801272ee79cfde7fa5960571fee36b9b-Abstract.html"},{"key":"768_CR60","unstructured":"Nguyen, H.H., Imine, A., Rusinowitch, M.: Network structure release under differential privacy. Trans. Data Priv. 9(3), 215\u2013241 (2016). http:\/\/www.tdp.cat\/issues16\/abs.a248a16.php"},{"key":"768_CR61","unstructured":"Paverd, A., Martin, A., Brown, I.: Modelling and automatically analysing privacy properties for honest-but-curious adversaries. Tech., Rep. (2014)"},{"key":"768_CR62","unstructured":"Pei, H., Wei, B., Chang, K.C., Lei, Y., Yang, B.: Geom-gcn: geometric graph convolutional networks. In: ICLR (2020)"},{"key":"768_CR63","doi-asserted-by":"publisher","unstructured":"Qin, Z., Bai, Y., Sun, Y.: Ghashing: semantic graph hashing for approximate similarity search in graph databases. In: Gupta, R., Liu, Y., Tang, J., Prakash, B.A. (eds.) KDD \u201920: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23\u201327, 2020, pp. 2062\u20132072. ACM (2020). https:\/\/doi.org\/10.1145\/3394486.3403257","DOI":"10.1145\/3394486.3403257"},{"key":"768_CR64","doi-asserted-by":"publisher","unstructured":"Qiu, R., Huang, Z., Li, J., Yin, H.: Exploiting cross-session information for session-based recommendation with graph neural networks. TOIS 38(3), 22:1\u201322:23 (2020). https:\/\/doi.org\/10.1145\/3382764","DOI":"10.1145\/3382764"},{"key":"768_CR65","doi-asserted-by":"publisher","unstructured":"Qiu, R., Yin, H., Huang, Z., Chen, T.: GAG: global attributed graph neural network for streaming session-based recommendation. In: SIGIR, pp. 669\u2013678 (2020). https:\/\/doi.org\/10.1145\/3397271.3401109","DOI":"10.1145\/3397271.3401109"},{"key":"768_CR66","doi-asserted-by":"publisher","unstructured":"Ragesh, R., Sellamanickam, S., Iyer, A., Bairi, R., Lingam, V.: Hetegcn: heterogeneous graph convolutional networks for text classification. In: WSDM, pp. 860\u2013868 (2021). https:\/\/doi.org\/10.1145\/3437963.3441746","DOI":"10.1145\/3437963.3441746"},{"key":"768_CR67","doi-asserted-by":"publisher","unstructured":"Rastogi, V., Hay, M., Miklau, G., Suciu, D.: Relationship privacy: output perturbation for queries with joins. In: PODS (2009). https:\/\/doi.org\/10.1145\/1559795.1559812","DOI":"10.1145\/1559795.1559812"},{"issue":"11","key":"768_CR68","first-page":"169","volume":"4","author":"RL Rivest","year":"1978","unstructured":"Rivest, R.L., Adleman, L., Dertouzos, M.L., et al.: On data banks and privacy homomorphisms. FSC 4(11), 169\u2013180 (1978)","journal-title":"FSC"},{"key":"768_CR69","unstructured":"Rossi, E., Frasca, F., Chamberlain, B., Eynard, D., Bronstein, M.M., Monti, F.: SIGN: scalable inception graph neural networks. CoRR (2020). arXiv:2004.11198"},{"key":"768_CR70","doi-asserted-by":"publisher","unstructured":"Rouhani, B.D., Riazi, M.S., Koushanfar, F.: Deepsecure: scalable provably-secure deep learning. In: DAC, pp. 2:1\u20132:6 (2018). https:\/\/doi.org\/10.1145\/3195970.3196023","DOI":"10.1145\/3195970.3196023"},{"key":"768_CR71","doi-asserted-by":"crossref","unstructured":"Sajadmanesh, S., Gatica-Perez, D.: When differential privacy meets graph neural networks. CoRR (2020). arXiv:2006.05535","DOI":"10.1145\/3460120.3484565"},{"key":"768_CR72","doi-asserted-by":"publisher","unstructured":"Scannapieco, M., Figotin, I., Bertino, E., Elmagarmid, A.K.: Privacy preserving schema and data matching. In: SIGMOD (2007). https:\/\/doi.org\/10.1145\/1247480.1247553","DOI":"10.1145\/1247480.1247553"},{"issue":"1","key":"768_CR73","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). https:\/\/doi.org\/10.1109\/TNN.2008.2005605","journal-title":"IEEE Trans. Neural Netw."},{"issue":"1","key":"768_CR74","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1007\/s41019-019-0086-8","volume":"4","author":"Y Shao","year":"2019","unstructured":"Shao, Y., Liu, J., Shi, S., Zhang, Y., Cui, B.: Fast de-anonymization of social networks with structural information. Data Sci. Eng. 4(1), 76\u201392 (2019). https:\/\/doi.org\/10.1007\/s41019-019-0086-8","journal-title":"Data Sci. Eng."},{"issue":"5","key":"768_CR75","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1109\/TKDE.2018.2847662","volume":"31","author":"S Sharma","year":"2019","unstructured":"Sharma, S., Powers, J., Chen, K.: Privategraph: privacy-preserving spectral analysis of encrypted graphs in the cloud. IEEE Trans. Knowl. Data Eng. 31(5), 981\u2013995 (2019). https:\/\/doi.org\/10.1109\/TKDE.2018.2847662","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"768_CR76","doi-asserted-by":"publisher","unstructured":"Sihag, V.K.: A clustering approach for structural k-anonymity in social networks using genetic algorithm. In: CUBE, pp. 701\u2013706 (2012). https:\/\/doi.org\/10.1145\/2381716.2381850","DOI":"10.1145\/2381716.2381850"},{"issue":"5","key":"768_CR77","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1142\/S0218488502001648","volume":"10","author":"L Sweeney","year":"2002","unstructured":"Sweeney, L.: k-Anonymity: a model for protecting privacy. IJUFKS 10(5), 557\u2013570 (2002). https:\/\/doi.org\/10.1142\/S0218488502001648","journal-title":"IJUFKS"},{"key":"768_CR78","doi-asserted-by":"publisher","unstructured":"Tang, X., Li, Y., Sun, Y., Yao, H., Mitra, P., Wang, S.: Transferring robustness for graph neural network against poisoning attacks. In: WSDM, pp. 600\u2013608 (2020). https:\/\/doi.org\/10.1145\/3336191.3371851","DOI":"10.1145\/3336191.3371851"},{"key":"768_CR79","doi-asserted-by":"publisher","unstructured":"Wang, H., Wang, N., Yeung, D.: Collaborative deep learning for recommender systems. In: Cao, L., Zhang, C., Joachims, T., Webb, G.I., Margineantu, D.D., Williams, G. (eds.) Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, August 10\u201313, 2015, pp. 1235\u20131244. ACM (2015). https:\/\/doi.org\/10.1145\/2783258.2783273","DOI":"10.1145\/2783258.2783273"},{"key":"768_CR80","unstructured":"Wang, Y., Wu, X.: Preserving differential privacy in degree-correlation based graph generation. Trans. Data Priv. 6(2), 127\u2013145 (2013). http:\/\/www.tdp.cat\/issues11\/abs.a113a12.php"},{"key":"768_CR81","doi-asserted-by":"publisher","unstructured":"Wang, Y., Wu, X., Wu, L.: Differential privacy preserving spectral graph analysis. In: PAKDD, vol. 7819, pp. 329\u2013340 (2013). https:\/\/doi.org\/10.1007\/978-3-642-37456-2_28","DOI":"10.1007\/978-3-642-37456-2_28"},{"key":"768_CR82","unstructured":"Wei, K., Li, J., Ma, C., Ding, M., Wei, S., Wu, F., Chen, G., Ranbaduge, T.: Vertical federated learning: challenges, methodologies and experiments. CoRR (2022). arXiv:2202.04309"},{"key":"768_CR83","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, F., Cao, Y., Huang, Y., Xie, X.: Fedgnn: federated graph neural network for privacy-preserving recommendation. CoRR (2021). arXiv:2102.04925","DOI":"10.1038\/s41467-022-30714-9"},{"key":"768_CR84","unstructured":"Wu, F., Jr., A.H.S., Zhang, T., Fifty, C., Yu, T., Weinberger, K.Q.: Simplifying graph convolutional networks. In: ICML (2019). http:\/\/proceedings.mlr.press\/v97\/wu19e.html"},{"key":"768_CR85","doi-asserted-by":"publisher","unstructured":"Wu, L., Yang, Y., Zhang, K., Hong, R., Fu, Y., Wang, M.: Joint item recommendation and attribute inference: An adaptive graph convolutional network approach. In: SIGIR, pp. 679\u2013688 (2020). https:\/\/doi.org\/10.1145\/3397271.3401144","DOI":"10.1145\/3397271.3401144"},{"key":"768_CR86","doi-asserted-by":"publisher","unstructured":"Wu, X., Li, F., Kumar, A., Chaudhuri, K., Jha, S., Naughton, J.F.: Bolt-on differential privacy for scalable stochastic gradient descent-based analytics. In: SIGMOD, pp. 1307\u20131322 (2017). https:\/\/doi.org\/10.1145\/3035918.3064047","DOI":"10.1145\/3035918.3064047"},{"key":"768_CR87","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. In: ICML, vol.\u00a080, pp. 5449\u20135458. PMLR (2018). http:\/\/proceedings.mlr.press\/v80\/xu18c.html"},{"key":"768_CR88","doi-asserted-by":"publisher","unstructured":"Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: SCAN: a structural clustering algorithm for networks. In: SIGKDD, pp. 824\u2013833 (2007). https:\/\/doi.org\/10.1145\/1281192.1281280","DOI":"10.1145\/1281192.1281280"},{"key":"768_CR89","doi-asserted-by":"publisher","unstructured":"Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. TIST (2), 12:1\u201312:19 (2019). https:\/\/doi.org\/10.1145\/3298981","DOI":"10.1145\/3298981"},{"key":"768_CR90","doi-asserted-by":"publisher","unstructured":"Ying, X., Wu, X.: Graph generation with prescribed feature constraints. In: SDM, pp. 966\u2013977 (2009). https:\/\/doi.org\/10.1137\/1.9781611972795.83","DOI":"10.1137\/1.9781611972795.83"},{"key":"768_CR91","doi-asserted-by":"publisher","unstructured":"Yu, F., Zhu, Y., Liu, Q., Wu, S., Wang, L., Tan, T.: TAGNN: target attentive graph neural networks for session-based recommendation. In: SIGIR, pp. 1921\u20131924 (2020). https:\/\/doi.org\/10.1145\/3397271.3401319","DOI":"10.1145\/3397271.3401319"},{"key":"768_CR92","doi-asserted-by":"publisher","unstructured":"Yuan, M., Chen, L., Rao, W., Mei, H.: A general framework for publishing privacy protected and utility preserved graph. In: ICDM, pp. 1182\u20131187 (2012). https:\/\/doi.org\/10.1109\/ICDM.2012.62","DOI":"10.1109\/ICDM.2012.62"},{"issue":"2","key":"768_CR93","doi-asserted-by":"publisher","first-page":"141","DOI":"10.14778\/1921071.1921080","volume":"4","author":"M Yuan","year":"2010","unstructured":"Yuan, M., Chen, L., Yu, P.S.: Personalized privacy protection in social networks. PVLDB 4(2), 141\u2013150 (2010). https:\/\/doi.org\/10.14778\/1921071.1921080","journal-title":"PVLDB"},{"key":"768_CR94","doi-asserted-by":"publisher","unstructured":"Yuan, M., Chen, L., Yu, P.S., Mei, H.: Privacy preserving graph publication in a distributed environment. WWW (2015). https:\/\/doi.org\/10.1007\/s11280-014-0290-4","DOI":"10.1007\/s11280-014-0290-4"},{"issue":"4","key":"768_CR95","doi-asserted-by":"publisher","first-page":"265","DOI":"10.14778\/2535570.2488333","volume":"6","author":"K Zeng","year":"2013","unstructured":"Zeng, K., Yang, J., Wang, H., Shao, B., Wang, Z.: A distributed graph engine for web scale RDF data. Proc. VLDB Endow. 6(4), 265\u2013276 (2013). https:\/\/doi.org\/10.14778\/2535570.2488333","journal-title":"Proc. VLDB Endow."},{"key":"768_CR96","doi-asserted-by":"publisher","unstructured":"Zhang, J., Cormode, G., Procopiuc, C.M., Srivastava, D., Xiao, X.: Private release of graph statistics using ladder functions. In: SIGMOD, pp. 731\u2013745 (2015). https:\/\/doi.org\/10.1145\/2723372.2737785","DOI":"10.1145\/2723372.2737785"},{"key":"768_CR97","doi-asserted-by":"publisher","unstructured":"Zhang, J., Zhang, Z., Xiao, X., Yang, Y., Winslett, M.: Functional mechanism: regression analysis under differential privacy. PVLDB 5(11), 1364\u20131375 (2012). https:\/\/doi.org\/10.14778\/2350229.2350253. http:\/\/vldb.org\/pvldb\/vol5\/p1364_junzhang_vldb2012.pdf","DOI":"10.14778\/2350229.2350253"},{"key":"768_CR98","doi-asserted-by":"publisher","unstructured":"Zhang, S., Yin, H., Chen, T., Nguyen, Q.V.H., Huang, Z., Cui, L.: Gcn-based user representation learning for unifying robust recommendation and fraudster detection. In: SIGIR, pp. 689\u2013698 (2020). https:\/\/doi.org\/10.1145\/3397271.3401165","DOI":"10.1145\/3397271.3401165"},{"key":"768_CR99","doi-asserted-by":"publisher","unstructured":"Zhang, W., Miao, X., Shao, Y., Jiang, J., Chen, L., Ruas, O., Cui, B.: Reliable data distillation on graph convolutional network. In: SIGMOD, pp. 1399\u20131414 (2020). https:\/\/doi.org\/10.1145\/3318464.3389706","DOI":"10.1145\/3318464.3389706"},{"key":"768_CR100","doi-asserted-by":"publisher","unstructured":"Zheng, D., Song, X., Ma, C., Tan, Z., Ye, Z., Dong, J., Xiong, H., Zhang, Z., Karypis, G.: DGL-KE: training knowledge graph embeddings at scale. In: SIGIR, pp. 739\u2013748 (2020). https:\/\/doi.org\/10.1145\/3397271.3401172","DOI":"10.1145\/3397271.3401172"},{"key":"768_CR101","doi-asserted-by":"publisher","unstructured":"Zhou, X., Sun, J., Li, G., Feng, J.: Query performance prediction for concurrent queries using graph embedding. Proc. VLDB Endow. 13(9), 1416\u20131428 (2020). https:\/\/doi.org\/10.14778\/3397230.3397238. http:\/\/www.vldb.org\/pvldb\/vol13\/p1416-zhou.pdf","DOI":"10.14778\/3397230.3397238"},{"key":"768_CR102","unstructured":"Zhu, L., Liu, Z., Han, S.: Deep leakage from gradients. In: NeurIPS, pp. 14747\u201314756 (2019)"},{"key":"768_CR103","doi-asserted-by":"publisher","unstructured":"Zou, L., Chen, L., \u00d6zsu, M.T.: K-automorphism: A general framework for privacy preserving network publication. PVLDB 2(1), 946\u2013957 (2009). https:\/\/doi.org\/10.14778\/1687627.1687734","DOI":"10.14778\/1687627.1687734"}],"container-title":["The VLDB Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-022-00768-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00778-022-00768-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-022-00768-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T02:05:41Z","timestamp":1685325941000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00778-022-00768-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,4]]},"references-count":103,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["768"],"URL":"https:\/\/doi.org\/10.1007\/s00778-022-00768-8","relation":{},"ISSN":["1066-8888","0949-877X"],"issn-type":[{"value":"1066-8888","type":"print"},{"value":"0949-877X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,4]]},"assertion":[{"value":"6 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}