• DocumentCode
    1566853
  • Title

    Privacy Preserving Support Vector Machines in Wireless Sensor Networks

  • Author

    Kim, Dong Seong ; Azim, Muhammad Anwarul ; Park, Jong Sou

  • Author_Institution
    Network & Embedded Security Lab., Korea Aerosp. Univ., Goyang
  • fYear
    2008
  • Firstpage
    1260
  • Lastpage
    1265
  • Abstract
    It is important to achieve energy efficient data mining in Wireless Sensor Networks (WSN) while preserving privacy of data. In this paper, we present a privacy preserving data mining based on Support Vector Machines (SVM). We review the previous approach in privacy preserving data mining in distributed system. And we also review energy efficient data mining in WSN. We then propose an energy efficient privacy preserving data mining in WSN. We use SVM because it has been shown best classification accuracy and sparse data presentation using support vectors. We show security analysis and energy estimation of our proposed approach.
  • Keywords
    data mining; data privacy; support vector machines; wireless sensor networks; data mining; data privacy preservation; sparse data presentation; support vector machines; wireless sensor networks; Availability; Commutation; Data mining; Data privacy; Data security; Energy efficiency; Kernel; Support vector machine classification; Support vector machines; Wireless sensor networks; Sensor networks; data mining; energy efficiency; privacy preserving data mining; security;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Availability, Reliability and Security, 2008. ARES 08. Third International Conference on
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-0-7695-3102-1
  • Type

    conf

  • DOI
    10.1109/ARES.2008.151
  • Filename
    4529488