With rapid development of big data storage and computing techniques, we have developed novel techniques beyond big data. Many aspects for both scientific research and people’s daily life have been influenced by big data based technology such as artificial intelligence, cloud computing, and Internet of Things. Providing security and privacy for big data storage, transmission, and processing have been attracting much attention in all big data related areas. IEEE Bigdatasecurity 2024 addresses this domain and aims to gather recent academic achievements in this field.
Security and robustness on Artificial Intelligence is the second concentration of IEEE Bigdatasecurity 2024. The emerging needs for building reliable and robust AI models in Big Data and Cloud environments with security and privacy guaranteed have attracted attention from a number of different perspectives. The new methods deployed in Big Data and Cloud environment have covered distinct dimensions, such as robust deep learning, secure deep learning/machine learning, multi-party computing, edge/fog computing, energy consumptions, high performance, and heterogeneous resources, cloud models, heterogeneous architecture, tele-health, resource allocation, load balance, multimedia, and QoS, etc.
General Chairs |
Meikang Qiu, Augusta University, USA |
Program Chairs |
Yonghao Wang, Birmingham City University, UK |
Xiaofu He, Columbia University, USA |
Yongxin Zhu, Shanghai Advanced Research Institute, China |
Industry Chair |
Peng Zhang, SUNY Stony Brook, USA |
Publicity Chairs |
Bo Li, Beihang University, China |
Keke Gai, Beijing Institute of Technology, China |
Md Liakat Ali, Rider University, USA |
Local Chairs |
Gang Zeng, Nagoya University, Japan |
Web Chair |
Yunhe Feng, University of North Texas, USA |
Financial Chair |
Hui Zhao, Henan University, China |
Award Chair |
Sun-Yuan Kung, Princeton University, USA |
Steering Committee |
Meikang Qiu (Chair), Augusta University, USA |
Sun-Yuan Kung, Princeton University, USA |
Ruqian Lu, Chinese Academy of Sciences | CAS·Academy of Mathematics and Systems Science, China |
Barbara Carminati, University of Insubria, Italy |
Technical Program Committee |
TBD |
Many novel techniques and applications are invented based on the rapid development of big data. Today, some aspects for both scientific research and people’s daily life have been influenced by big data based technology such as artificial intelligence, cloud computing, and Internet of Things. Providing security and privacy for big data storage, transmission, and processing have been attracting much attention in all big data related areas. IEEE Bigdatasecurity 2024 addresses this domain and aims to gather recent academic achievements in this field.
Security and robustness on Artificial Intelligence is the second concentration of IEEE Bigdatasecurity 2024. The emerging needs for building reliable and robust AI models in Big Data and Cloud environments with security and privacy guaranteed have attracted attention from a number of different perspectives. The new methods deployed in Big Data and Cloud environment have covered distinct dimensions, such as robust deep learning, secure deep learning/machine learning, multi-party computing, edge/fog computing, energy consumptions, high performance, and heterogeneous resources, cloud models, heterogeneous architecture, tele-health, resource allocation, load balance, multimedia, and QoS, etc.
05月10日
2024
05月12日
2024
初稿截稿日期
注册截止日期
2022年05月06日 中国 Jinan
The 8th IEEE International Conference on Big Data Security on Cloud2021年05月15日 美国 New York
The 7th IEEE International Conference on Big Data Security on Cloud2016年04月09日 美国 New York
第二届IEEE云大数据安全国际会议
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