Private and Scalable Personal Data Analytics using a Hybrid Edge-Cloud Deep Learning
| Title | Private and Scalable Personal Data Analytics using a Hybrid Edge-Cloud Deep Learning |
| Publication Type | Journal Article |
| Year of Publication | 2018 |
| Authors | Osia, S. A., A. S. Shamsabadi, H. R. Rabiee, and H. Haddadi |
| Journal | IEEE Computer |
| Volume | 51 |
| Issue | 5 |
| Start Page | 42 |
| Pagination | 42-49 |
| Date Published | 05/2018 |
| ISSN | 1558-0814 |
| Accession Number | 17788152 |
| Other Numbers | Print ISSN: 0018-9162 |
| Keywords | Deep Learning, Edge Computing, Privacy |
| Abstract | 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. |
| DOI | <a href="http://dx.doi.org/10.1109/MC.2018.2381113&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; |
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