{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T17:35:52Z","timestamp":1730223352756,"version":"3.28.0"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1109\/globecom46510.2021.9685087","type":"proceedings-article","created":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T21:59:04Z","timestamp":1643839144000},"page":"01-06","source":"Crossref","is-referenced-by-count":1,"title":["Pri-PGD: Forging privacy-preserving graph towards spectral-based graph neural network"],"prefix":"10.1109","author":[{"given":"Yong","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Cyber Engineering, Xidian University,Xi&#x0027;an,Shaanxi,China,710070"}]},{"given":"Yixin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University,Xi&#x0027;an,Shaanxi,China,710070"}]},{"given":"Jiale","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University,Xi&#x0027;an,Shaanxi,China,710070"}]},{"given":"Jianfeng","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University,Xi&#x0027;an,Shaanxi,China,710070"}]},{"given":"Zhihong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University,Xi&#x0027;an,Shaanxi,China,710070"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Sgnn: A graph neural network based federated learning approach by hiding structure","author":"guangxu","year":"0","journal-title":"2019 IEEE International Conference on Big Data (Big Data)"},{"key":"ref11","first-page":"1","article-title":"ASFGNN: Automated separated-federated graph neural network","author":"longfei","year":"2021","journal-title":"Peer-to-Peer Networking and Applications"},{"key":"ref12","article-title":"Locally Private Graph Neural Networks","volume":"abs 2006 5535 12","author":"sina","year":"2020","journal-title":"CoRR"},{"key":"ref13","article-title":"Privacy-Preserving Graph Convolutional Networks for Text Classification","author":"timour","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref14","article-title":"Semi-Supervised Classification with Graph Convolutional Networks","author":"kipf","year":"0","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref15","article-title":"Graph attention networks","author":"petar","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2235192"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2238935"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2015.2469645"},{"key":"ref19","article-title":"A comprehensive survey on graph neural networks","author":"zonghan","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"ref4","article-title":"Graph-based privacy-preserving data publication","author":"xiang-yang","year":"0","journal-title":"IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications"},{"key":"ref3","first-page":"2016","article-title":"Regulation (EU) 2016\/679 of the European Parliament and of the Council","volume":"679","year":"2016","journal-title":"Regulation (EU)"},{"key":"ref6","article-title":"Privacy-preserving query over encrypted graph-structured data in cloud computing","author":"ning","year":"0","journal-title":"2011 31st International Conference on Distributed Computing Systems"},{"key":"ref5","article-title":"Differential privacy preserving spectral graph analysis","author":"yue","year":"2013","journal-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining"},{"key":"ref8","first-page":"341","article-title":"A survey of graph-modification techniques for privacy-preserving on networks","volume":"47 3","author":"jordi","year":"2017","journal-title":"Artificial Intelligence Review"},{"key":"ref7","article-title":"Privacy in social networks: How risky is your social graph?","author":"cuneyt","year":"0","journal-title":"2012 IEEE 28th International Conference on Data Engineering"},{"key":"ref2","first-page":"121","article-title":"New forms of social and professional digital relationships: the case of Facebook","volume":"2 2","author":"fernando","year":"2012","journal-title":"Social Network Analysis and Mining"},{"key":"ref9","article-title":"Mining frequent graph patterns with differential privacy","author":"entong","year":"0","journal-title":"Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"jie","year":"2020","journal-title":"Open AI"},{"key":"ref20","article-title":"Leon Linear Algebra with Applications 7th edition","author":"steven","year":"2006","journal-title":"Bargain Smart Plug"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511546891"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-53622-3"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/0021-8693(80)90309-9"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1038\/44565"},{"key":"ref25","first-page":"201","article-title":"Revisiting semi-supervised learning with graph embeddings","author":"zhilin","year":"0","journal-title":"International Conference on Machine Learning"}],"event":{"name":"GLOBECOM 2021 - 2021 IEEE Global Communications Conference","start":{"date-parts":[[2021,12,7]]},"location":"Madrid, Spain","end":{"date-parts":[[2021,12,11]]}},"container-title":["2021 IEEE Global Communications Conference (GLOBECOM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9685019\/9685006\/09685087.pdf?arnumber=9685087","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T20:23:34Z","timestamp":1654547014000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9685087\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/globecom46510.2021.9685087","relation":{},"subject":[],"published":{"date-parts":[[2021,12]]}}}