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Private and Scalable Personal Data Analytics Using Hybrid Edge-to-Cloud Deep Learning.

Although the ability to collect, collate, and analyze the vast amount of data generated from cyber-physical systems and Internet of Things devices can be beneficial to both users and industry, this process has led to a number of challenges, including privacy and scalability issues. The authors prese...

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Publicado en:Computer (00189162) Vol. 51; no. 5; pp. 42 - 50
Autores principales: Osia, Seyed Ali, Shamsabadi, Ali Shahin, Taheri, Ali, Rabiee, Hamid R., Haddadi, Hamed
Formato: Artículo
Publicado: IEEE May2018
Materias:
Personally identifiable information
Data analytics
Cloud computing
Deep learning
Data privacy
Acceso en línea:Ver este registro en EBSCOhost
Sumario:Although the ability to collect, collate, and analyze the vast amount of data generated from cyber-physical systems and Internet of Things devices can be beneficial to both users and industry, this process has led to a number of challenges, including privacy and scalability issues. The authors present a hybrid framework where user-centered edge devices and resources can complement the cloud for providing privacy-aware, accurate, and efficient analytics.