• Login
    View Item 
    •   Home
    • Research Publications
    • College of Engineering & Technology
    • Department of Electronics, Computing & Maths
    • View Item
    •   Home
    • Research Publications
    • College of Engineering & Technology
    • Department of Electronics, Computing & Maths
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UDORACommunitiesTitleAuthorsIssue DateSubmit DateSubjectsThis CollectionTitleAuthorsIssue DateSubmit DateSubjects

    My Account

    LoginRegister

    About and further information

    AboutOpen Access WebpagesOpen Access PolicyTake Down Policy Quick Guide for Submissions - Doctoral StudentsUniversity NewsTools for ResearchersLibraryUDo

    Statistics

    Display statistics

    Improved Kalman filter based differentially private streaming data release in cognitive computing.

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Publisher version
    View Source
    Access full-text PDFOpen Access
    View Source
    Check access options
    Check access options
    Authors
    Wang, Jun
    Luo, Jing
    Liu, Xiaozhu
    Li, Yongkai
    Liu, Shubo
    Zhu, Rongbo
    Anjum, Ashiq
    Affiliation
    University of Derby
    South-Central University for Nationalities
    Wuhan University of Technology
    Wuhan University
    Issue Date
    2019-04-04
    
    Metadata
    Show full item record
    Abstract
    Cognitive computing works well based on volumes of data, which offers the guarantee of unlocking novel insights and data-driven decisions. Steaming data is a major component of aggregated data, and sharing these real-time aggregated statistics has gained a lot of benefits in decision analysis, such as traffic heat map and disease outbreaks. However, original streaming data sharing will bring users the risk of privacy disclosure. In this paper, differential privacy technology is introduced into cognitive system, and an improved Kalman filter based differentially private streaming data release scheme is proposed for privacy requirement of cognitive computing system. The feasibility of the proposed scheme has been demonstrated through analysis of the utility of sanitized data from four real datasets, and the experimental results show that the proposed scheme outperforms the Kalman filter-based method at the same level of privacy preserving.
    Citation
    Wang, J. et al. (2019) 'Improved Kalman filter based differentially private streaming data release in cognitive computing', Future Generation Computer Systems, 98, pp.541-549. doi: 10.1016/j.future.2019.03.050.
    Publisher
    Elsevier
    Journal
    Future Generation Computer Systems
    URI
    http://hdl.handle.net/10545/623984
    DOI
    10.1016/j.future.2019.03.050
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0167739X18330838
    Type
    Article
    Language
    en
    ISSN
    0167739X
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.future.2019.03.050
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

    entitlement

     
    DSpace software (copyright © 2002 - 2019)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.