{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T13:17:59Z","timestamp":1761311879507,"version":"3.37.3"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001126"],"award-info":[{"award-number":["62001126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Federated generative adversarial networks are designed to collaborate across the communication and privacy-constrained edge servers participating in training. However, in the Internet of Things scenario, local updates uploaded by edge servers can lead to the risk of privacy breaches. Gradient-sanitized-based approaches can transmit sanitized sensitive data with strict privacy guarantees, but gradient clipping and perturbation severely degrade convergence performance. In this paper, our proposed algorithm enhances the privacy of terminated raw data through differential privacy before it is transmitted to the edge server. The edge server trains the local generator and discriminator using the perturbed data, which provides privacy guarantees for the gradient attack on the FedGAN without compromising the gradient accuracy. The results of the experimental evaluation show that the algorithm generates images with slightly better quality than that generated by the gradient-sanitized-based approaches while maintaining privacy.<\/jats:p>","DOI":"10.1093\/comjnl\/bxac060","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T11:13:14Z","timestamp":1649934794000},"page":"2860-2869","source":"Crossref","is-referenced-by-count":10,"title":["Privacy-Enhanced Federated Generative Adversarial Networks for Internet of Things"],"prefix":"10.1093","volume":"65","author":[{"given":"Qingkui","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Computer and Software , Nanjing University of Information Science and Technology, China"}]},{"given":"Liwen","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer and Software , Nanjing University of Information Science and Technology, China"}]},{"given":"Zhuotao","family":"Lian","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering , the University of Aizu, Japan"}]},{"given":"Huakun","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Cyber Engineering , Guangzhou University, China"}]},{"given":"Jung Yoon","family":"Kim","sequence":"additional","affiliation":[{"name":"College of Future Industry , Gachon University, South Korea"}]}],"member":"286","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"volume-title":"IEEE transactions on industrial informatics","year":"2021","author":"Wang","key":"2022111713012956600_ref1"},{"key":"2022111713012956600_ref2","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/j.future.2017.12.002","article-title":"Enabling IoT platforms for social IoT applications: Vision, feature mapping, and challenges","volume":"92","author":"Afzal","year":"2019","journal-title":"Future Generation Computer Systems"},{"volume-title":"IEEE Journal of Biomedical and Health Informatics","year":"2021","author":"Hu","key":"2022111713012956600_ref3"},{"volume-title":"IEEE Internet of Things Journal","year":"2021","author":"Wang","key":"2022111713012956600_ref4"},{"volume-title":"IEEE transactions on industrial informatics","year":"2021","author":"Wang","key":"2022111713012956600_ref5"},{"key":"2022111713012956600_ref6","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2019.100129","article-title":"A survey on internet of things security: Requirements, challenges, and solutions","volume":"14","author":"HaddadPajouh","year":"2021","journal-title":"Internet of Things"},{"key":"2022111713012956600_ref7","first-page":"1332","volume-title":"IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","author":"Wang","year":"2020"},{"volume-title":"IEEE Transactions on Neural Networks and Learning Systems","year":"2022","author":"Gao","key":"2022111713012956600_ref8"},{"key":"2022111713012956600_ref9","doi-asserted-by":"crossref","DOI":"10.1007\/s11036-021-01846-x","article-title":"Sdtioa: Modeling the timed privacy requirements of IoT service composition: A user interaction perspective for automatic transformation from bpel to timed automata","volume-title":"Mobile Networks and Applications","author":"Gao","year":"2021"},{"key":"2022111713012956600_ref10","doi-asserted-by":"crossref","first-page":"153826","DOI":"10.1109\/ACCESS.2020.3018170","article-title":"Research on artificial intelligence enhancing internet of things security: A survey","volume":"8","author":"Hui","year":"2020","journal-title":"Ieee Access"},{"key":"2022111713012956600_ref11","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"2022111713012956600_ref12","article-title":"PHIAF: prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion","volume":"23","author":"Li","year":"2022","journal-title":"Brief. Bioinform."},{"volume-title":"IEEE Transactions on Computational Social Systems","year":"2021","author":"Nie","key":"2022111713012956600_ref13"},{"key":"2022111713012956600_ref14","first-page":"1273","volume-title":"Artificial intelligence and statistics","author":"McMahan","year":"2017"},{"volume-title":"IEEE Transactions on Network Science and Engineering","year":"2022","author":"Song","key":"2022111713012956600_ref15"},{"key":"2022111713012956600_ref16","first-page":"1","volume-title":"GLOBECOM 2020-2020 IEEE Global Communications Conference","author":"Zixu","year":"2020"},{"volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems","year":"2020","author":"Zhu","key":"2022111713012956600_ref17"},{"key":"2022111713012956600_ref18","article-title":"Gs-wgan: A gradient-sanitized approach for learning differentially private generators","volume-title":"Advances in Neural Information Processing Systems","author":"Chen","year":"2020"},{"key":"2022111713012956600_ref19","first-page":"50","article-title":"Federated learning: Challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Processing Magazine"},{"article-title":"Webfed: Cross-platform federated learning framework based on web browser with local differential privacy","year":"2021","author":"Lian","key":"2022111713012956600_ref20"},{"key":"2022111713012956600_ref21","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2021.108122","article-title":"Efficient and flexible management for industrial internet of things: A federated learning approach","volume":"192","author":"Guo","year":"2021","journal-title":"Computer Networks"},{"key":"2022111713012956600_ref22","first-page":"1","volume-title":"GLOBECOM 2020-2020 IEEE Global Communications Conference","author":"Liu","year":"2020"},{"key":"2022111713012956600_ref23","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1587\/transinf.2021BCP0006","article-title":"The Institute of Electronics, Information and Communication Engineers","volume":"105","author":"Lian","year":"2022","journal-title":"IEICE Transactions on Information and Systems"},{"key":"2022111713012956600_ref24","doi-asserted-by":"crossref","first-page":"2462","DOI":"10.1109\/COMST.2020.3009103","article-title":"Edge computing in industrial internet of things: Architecture, advances and challenges","volume":"22","author":"Qiu","year":"2020","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"2022111713012956600_ref25","doi-asserted-by":"crossref","first-page":"4002","DOI":"10.1109\/TNSM.2021.3125395","article-title":"Real-time multiple-workflow scheduling in cloud environments","volume":"18","author":"Ma","year":"2021","journal-title":"IEEE Transactions on Network and Service Management"},{"key":"2022111713012956600_ref26","doi-asserted-by":"crossref","first-page":"9372","DOI":"10.1109\/JIOT.2020.2986015","article-title":"Intelligent cooperative edge computing in internet of things","volume":"7","author":"Gong","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"2022111713012956600_ref27","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1109\/TGCN.2021.3067374","article-title":"Ssur: An approach to optimizing virtual machine allocation strategy based on user requirements for cloud data center","volume":"5","author":"Huang","year":"2021","journal-title":"IEEE Transactions on Green Communications and Networking"},{"key":"2022111713012956600_ref28","doi-asserted-by":"crossref","first-page":"209191","DOI":"10.1109\/ACCESS.2020.3038287","article-title":"Edgefed: Optimized federated learning based on edge computing","volume":"8","author":"Ye","year":"2020","journal-title":"IEEE Access"},{"key":"2022111713012956600_ref29","doi-asserted-by":"crossref","first-page":"3310","DOI":"10.1109\/JIOT.2020.3023126","article-title":"Poisongan: Generative poisoning attacks against federated learning in edge computing systems","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"2022111713012956600_ref30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TITS.2021.3098355","article-title":"A hybrid approach to trust node assessment and management for vanets cooperative data communication: Historical interaction perspective","author":"Gao","year":"2021","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"2022111713012956600_ref31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2020.04.026","article-title":"Privacy-aware offloading for training tasks of generative adversarial network in edge computing","volume":"532","author":"Xiaolong","year":"2020","journal-title":"Inform. Sci."},{"article-title":"Fedcg: Leverage conditional gan for protecting privacy and maintaining competitive performance in federated learning","year":"2021","author":"Wu","key":"2022111713012956600_ref32"},{"article-title":"Fedgan: Federated generative adversarial networks for distributed data","year":"2020","author":"Rasouli","key":"2022111713012956600_ref33"},{"volume-title":"Theory and Applications of Models of Computation. TAMC 2008. Lecture Notes in Computer Science","year":"2008","author":"Dwork","key":"2022111713012956600_ref34"},{"volume-title":"International conference on learning representations","year":"2018","author":"Jordon","key":"2022111713012956600_ref35"},{"article-title":"Scalable differentially private generative student model via pate","year":"2019","author":"Long","key":"2022111713012956600_ref36"},{"article-title":"Differentially private generative adversarial network","year":"2018","author":"Xie","key":"2022111713012956600_ref37"},{"key":"2022111713012956600_ref38","first-page":"1","article-title":"Feddpgan: Federated differentially private generative adversarial networks framework for the detection of covid-19 pneumonia","author":"Zhang","year":"2021","journal-title":"Information Systems Frontiers"},{"key":"2022111713012956600_ref39","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"Arjovsky","year":"2017","journal-title":"International conference on machine learning"},{"key":"2022111713012956600_ref40","first-page":"30","article-title":"Improved training of wasserstein gans","author":"Gulrajani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"2022111713012956600_ref41","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1145\/2976749.2978318","volume-title":"Proceedings of the 2016 ACM SIGSAC conference on computer and communications security","author":"Abadi","year":"2016"},{"key":"2022111713012956600_ref42","first-page":"2","article-title":"Mnist handwritten digit database","author":"LeCun","year":"2010","journal-title":"ATT Labs [Online]. Available"},{"volume-title":"Advances in neural information processing systems","year":"2020","author":"Heusel","key":"2022111713012956600_ref43"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/65\/11\/2860\/47089040\/bxac060.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/65\/11\/2860\/47089040\/bxac060.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T13:03:06Z","timestamp":1668690186000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/65\/11\/2860\/6580518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,4]]},"references-count":43,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,5,4]]},"published-print":{"date-parts":[[2022,11,11]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxac060","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"type":"print","value":"0010-4620"},{"type":"electronic","value":"1460-2067"}],"subject":[],"published-other":{"date-parts":[[2022,11]]},"published":{"date-parts":[[2022,5,4]]}}}