Please use this identifier to cite or link to this item:
http://ir.mu.ac.ke:8080/jspui/handle/123456789/7915
Title: | Differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems: State-of-the-art and perspectives |
Authors: | K. Tarus, Samwel |
Keywords: | Data fusion Private data Deep learning Cyber–physical system Cyber–social system Computing system |
Issue Date: | Dec-2021 |
Publisher: | Elsevier |
Abstract: | The modern technological advancement influences the growth of the cyber–physical system and cyber–social system to a more advanced computing system cyber–physical–social system (CPSS). Therefore, CPSS leads the data science revolution by promoting tri-space information resource from a single space. The establishment of CPSSs increases the related privacy concerns. To provide privacy on CPSSs data, various privacy-preserving schemes have been introduced in the recent past. However, technological advancement in CPSSs requires the modifications of previous techniques to suit its dynamics. Meanwhile, differential privacy has emerged as an effective method to safeguard CPSSs data privacy. To completely comprehend the state-of-the-art developments and learn the field’s research directions, this article provides a comprehensive review of differentially private data fusion and deep learning in CPSSs. Additionally, we present a novel differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems , and various future research directions for CPSSs. |
URI: | https://doi.org/10.1016/j.inffus.2021.04.017 http://ir.mu.ac.ke:8080/jspui/handle/123456789/7915 |
Appears in Collections: | School of Biological and Physical Sciences |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.