{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T18:19:24Z","timestamp":1770229164005,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819561957","type":"print"},{"value":"9789819561964","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-6196-4_16","type":"book-chapter","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T05:59:20Z","timestamp":1770184760000},"page":"223-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-Preserving Graph Data Deduplication for\u00a0Deep Graph Learning"],"prefix":"10.1007","author":[{"given":"Zhibo","family":"Xu","sequence":"first","affiliation":[]},{"given":"Yiming","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Shangqi","family":"Lai","sequence":"additional","affiliation":[]},{"given":"Xiaoning","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Alsharif","family":"Abuadbba","sequence":"additional","affiliation":[]},{"given":"Xingliang","family":"Yuan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Boyle, E.,\u00a0Gilboa, N.,\u00a0Ishai, Y.: Function secret sharing: improvements and extensions. In: ACM CCS (2016)","DOI":"10.1145\/2976749.2978429"},{"key":"16_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/978-3-030-36030-6_14","volume-title":"Theory of Cryptography","author":"E Boyle","year":"2019","unstructured":"Boyle, E., Gilboa, N., Ishai, Y.: Secure computation with preprocessing via function secret sharing. In: Hofheinz, D., Rosen, A. (eds.) TCC 2019. LNCS, vol. 11891, pp. 341\u2013371. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-36030-6_14"},{"key":"16_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1007\/978-3-030-77886-6_30","volume-title":"Advances in Cryptology \u2013 EUROCRYPT 2021","author":"E Boyle","year":"2021","unstructured":"Boyle, E., et al.: Function secret sharing for mixed-mode and fixed-point secure computation. In: Canteaut, A., Standaert, F.-X. (eds.) EUROCRYPT 2021. LNCS, vol. 12697, pp. 871\u2013900. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-77886-6_30"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Wu, L., et\u00a0al.: Graph neural networks: foundation, frontiers and applications. In: KDD \u201922 (2022)","DOI":"10.1007\/978-981-16-6054-2"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Riazi, M.S., et\u00a0al.: Chameleon: a hybrid secure computation framework for machine learning applications. In: ACM AsiaCCS (2018)","DOI":"10.1145\/3196494.3196522"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Ying, R., et\u00a0al.: Graph convolutional neural networks for web-scale recommender systems. In: ACM KDD (2018)","DOI":"10.1145\/3219819.3219890"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Falzon, F., Paterson, K.G.: An efficient query recovery attack against a graph encryption scheme. In: ESORICS (2022)","DOI":"10.1007\/978-3-031-17140-6_16"},{"key":"16_CR8","unstructured":"Juvekar, C.,\u00a0Vaikuntanathan, V., Chandrakasan, A.P.: GAZELLE: a low latency framework for secure neural network inference. In: USENIX Security (2018)"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Keller, M.: MP-SPDZ: a versatile framework for multi-party computation. In: ACM CCS (2020)","DOI":"10.1145\/3372297.3417872"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Liu, J.,\u00a0Juuti, M.,\u00a0Lu, Y.,\u00a0Asokan, N.: Oblivious neural network predictions via MiniONN transformations. In: ACM CCS (2017)","DOI":"10.1145\/3133956.3134056"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Mishra, P.,\u00a0Lehmkuhl, R.,\u00a0Srinivasan, A.,\u00a0Zheng, W., Popa, R.A.: Delphi: a cryptographic inference service for neural networks. In: USENIX Security (2020)","DOI":"10.1145\/3411501.3419418"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Mohassel, P.,\u00a0Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning. In: IEEE S &P (2017)","DOI":"10.1109\/SP.2017.12"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Nayak, K.,\u00a0Wang, X.,\u00a0Ioannidis, S.,\u00a0Weinsberg, U.,\u00a0Taft, N.,\u00a0Shi, E.: Graphsc: parallel secure computation made easy. In: IEEE Symposium on Security and Privacy, pp. 377\u2013394. IEEE Computer Society (2015)","DOI":"10.1109\/SP.2015.30"},{"key":"16_CR14","unstructured":"Riazi, M.S., et\u00a0al.: XONN: XNOR-based oblivious deep neural network inference. In: USENIX Security (2019)"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Ryffel, T.,\u00a0Tholoniat, P.,\u00a0Pointcheval, D., Bach, F.R.: AriaNN: low-interaction privacy-preserving deep learning via function secret sharing. In: PoPETs (2022)","DOI":"10.2478\/popets-2022-0015"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: IEEE S &P. IEEE Computer Society (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Wu, B.,\u00a0Yang, X.,\u00a0Pan, S.,\u00a0Yuan, X.: Adapting membership inference attacks to GNN for graph classification: approaches and implications. In: IEEE ICDM (2021)","DOI":"10.1109\/ICDM51629.2021.00182"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Wu, B.,\u00a0Yang, X.,\u00a0Pan, S.,\u00a0Yuan, X.: Model extraction attacks on graph neural networks: taxonomy and realisation. In: ACM AsiaCCS (2022)","DOI":"10.1145\/3488932.3497753"},{"key":"16_CR19","unstructured":"Xu, Z.,\u00a0Lai, S.,\u00a0Liu, X.,\u00a0Abuadbba, A.,\u00a0Yuan, X.,\u00a0Yi, X.: OblivGNN: oblivious inference on transductive and inductive graph neural network. In: 33rd USENIX Security Symposium (USENIX Security 24) (2024)"},{"key":"16_CR20","unstructured":"Zhang, J.,\u00a0Wang, H.,\u00a0Zhu, M.: Build a GNN-based real-time fraud detection solution using amazon SageMaker, Amazon Neptune, and the Deep Graph Library (2022). https:\/\/aws.amazon.com\/blogs\/machine-learning\/build-a-gnn-based-real-time-fraud-detection-solution-using-amazon-sagemaker-amazon-neptune-and-the-deep-graph-library\/"},{"key":"16_CR21","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.690049","volume":"12","author":"X Zhang","year":"2021","unstructured":"Zhang, X., Liang, L., Liu, L., Tang, M.: Graph neural networks and their current applications in bioinformatics. Front. Genet. 12, 690049 (2021)","journal-title":"Front. Genet."}],"container-title":["Lecture Notes in Computer Science","Databases Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-6196-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T05:59:25Z","timestamp":1770184765000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-6196-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819561957","9789819561964"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-6196-4_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"5 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Database Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"36","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adc-conference.github.io\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}