Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Guo, Ruoyang"'
The proliferation of location-based services and applications has brought significant attention to data and location privacy. While general secure computation and privacy-enhancing techniques can partially address this problem, one outstanding challe
Externí odkaz:
http://arxiv.org/abs/2408.07916
Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. To ensure users' privacy, differentially private federated learning has been intensively studied. The exis
Externí odkaz:
http://arxiv.org/abs/2009.08063
Publikováno v:
In High-Confidence Computing June 2022 2(2)
Akademický článek
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Akademický článek
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Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-1
Group $k$ k -nearest neighbor ( $k$ k GNN) search allows a group of $n$ n mobile users to jointly retrieve $k$ k points from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::67a445fc74e9c87366fe2aac17168182
https://doi.org/10.1109/tkde.2019.2930696
https://doi.org/10.1109/tkde.2019.2930696
Autor:
Guo, Ruilong1 (AUTHOR), Feng, Ruoyang2 (AUTHOR), Yang, Jiong1 (AUTHOR), Xiao, Yanfeng1 (AUTHOR) xiaoyanfenggroup@sina.com, Yin, Chunyan1 (AUTHOR) yinchunyan0624@sina.com
Publikováno v:
Scientific Reports. 4/10/2024, Vol. 14 Issue 1, p1-9. 9p.
Autor:
Guo, Yueqin1 (AUTHOR), Hu, Ruoyang1 (AUTHOR), Li, Naikang1 (AUTHOR), Li, Nannan1 (AUTHOR), Wu, Jiangli1 (AUTHOR), Yu, Huimin1 (AUTHOR), Tan, Jing1 (AUTHOR), Li, Zhouhua2 (AUTHOR) zhli@cnu.edu.cn, Xu, Shufa1 (AUTHOR) zhli@cnu.edu.cn
Publikováno v:
International Journal of Molecular Sciences. Feb2023, Vol. 24 Issue 3, p1926. 21p.
Publikováno v:
Web of Science
Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. To ensure users' privacy, differentially private federated learning has been intensively studied. The exis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56d1f23736132eabb18cba650a6af158
https://publons.com/wos-op/publon/56459224/
https://publons.com/wos-op/publon/56459224/
Autor:
Zhou, Qiang1 (AUTHOR), Liu, Feng2 (AUTHOR), Guo, Luoying3 (AUTHOR), Chen, Ruoyang4 (AUTHOR), Yuan, Xiaodong4 (AUTHOR), Li, Chao5 (AUTHOR), Shu, Liping2 (AUTHOR), Liu, Haitao2 (AUTHOR), Zhou, Yang6 (AUTHOR), Wu, Yu6 (AUTHOR), Shi, Haifeng6 (AUTHOR), Zhao, Hongwen1 (AUTHOR) zhaohw212@126.com, Jiang, Tingya2 (AUTHOR) jiangtingya@allograftdx.com
Publikováno v:
Nephrology. Aug2021, Vol. 26 Issue 8, p684-691. 8p.