Información de la conferencia

BigDataSecurity 2027: IEEE International Conference on Big Data Security on Cloud

Día de Entrega:
2027-02-01 Faltan 80 días
Fecha de Notificación:
2027-03-01
Fecha de conferencia:
2027-05-28
Ubicación:
Shanghai, China
Ediciones:
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56,1 / 100
Puesto n.º 816 de 5.776 congresos · 15% superior

N.º 42 de 340 en Minería de datos y bases de datos N.º 67 de 367 en Seguridad y privacidad

Reconocimiento académico (35%) Sin datos: se puntúa con la línea base neutra de 50 —
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
71
Atención de la comunidad (10%)
39
Integridad del registro público (15%)
70

Datos utilizados: Ediciones documentadas: 13 · Investigadores que lo siguen aquí: 11 · Investigadores que abrieron esta página en los últimos 24 meses: 6

Falta en el registro público: Tasas de aceptación históricas (+4,5)
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Solicitud de Artículos

BigDataSecurity 2027 (IEEE International Conference on Big Data Security on Cloud) is an academic conference held in Shanghai, China on 2027-05-28. The paper submission deadline is 2027-02-01. Acceptance notifications are sent on 2027-03-01.

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 2027 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 2027. 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. IEEE Bigdatasecurity 2027 is the next event after a series of successful academic get-togethers, including BigDataSecurity 2026 (New York, USA), BigDataSecurity 2025 (New York, USA), BigDataSecurity 2024 (New York, USA), BigDataSecurity 2023 (New York, USA), BigDataSecurity 2022 (Jinan, China), BigDataSecurity 2021 (New York, USA), BigDataSecurity 2020 (Baltimore, USA), BigDataSecurity 2019 (Washington, DC, USA), BigDataSecurity 2018 (Omaha, NE, USA), BigDataSecurity 2017 (New York, USA) , BigDataSecurity 2016 (Beijing, China), and BigDataSecurity 2015 (New York, USA). Topics of particular interest include, but are not limited to: Artificial intelligence security Novel big data security issues Novel big data privacy issues Blockchain-based security mechanism Blockchain-based big data sharing Security and privacy issues in blockchain Big data security issues in IoT Big data security issues in cloud computing Big data privacy in cloud computing Big data storage, integration, service, mining Virtualization for big data on cloud MapReduce with cloud for big data processing Heterogeneous architecture for cloud computing Dynamic resource sharing algorithm for cloud computing Load balance for cloud computing Mobile cloud computing Mobile commerce security and privacy Green cloud computing Embedded system security Cyber Security in emergent technologies Cyber hacking, next generation fire wall Cyber monitoring, incident response Database security, data center security Cyber threat intelligence Sensor network security in cloud computing Security policy and legal considerations Cloud and networking security Cloud computing and networking models
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