Issue No. 05 - May (2018 vol. 51)
Seyed Ali Osia , Sharif University of Technology
Ali Shahin Shamsabadi , Queen Mary University of London
Ali Taheri , Sharif University of Technology
Hamid R. Rabiee , Sharif University of Technology
Hamed Haddadi , Imperial College London
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.
cloud computing, data analysis, data privacy, Internet of Things
S. A. Osia, A. S. Shamsabadi, A. Taheri, H. R. Rabiee and H. Haddadi, "Private and Scalable Personal Data Analytics Using Hybrid Edge-to-Cloud Deep Learning," in Computer, vol. 51, no. 5, pp. 42-49, 2018.