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...
| Publicado en: | Computer (00189162) Vol. 51; no. 5; pp. 42 - 50 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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IEEE
May2018
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=129840999&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 129840999 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00189162 PUT jtl: Computer (00189162) issn: 00189162 maglogo: N pubinfo: dt: May2018 vid: 51 iid: 5 pid: 13605 pub: IEEE artinfo: ui: 129840999 10.1109/MC.2018.2381113 ppf: 42 ppct: 8 formats: tig: atl: Private and Scalable Personal Data Analytics Using Hybrid Edge-to-Cloud Deep Learning. aug: au: Osia, Seyed Ali Shamsabadi, Ali Shahin Taheri, Ali Rabiee, Hamid R. Haddadi, Hamed affil: Sharif University of Technology Queen Mary University of London Imperial College London su: Personally identifiable information Data analytics Cloud computing Deep learning Data privacy sug: subj: Personally identifiable information Data analytics Cloud computing Deep learning Data privacy keyword: analytics cloud computing cyber-physical systems deep learning edge computing Internet of Things IoT mobile and embedded deep learning privacy security ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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