• DocumentCode
    244817
  • Title

    Secure and Efficient Data Integrity Based on Iris Features in Cloud Computing

  • Author

    Abbdal, Salah H. ; Hai Jin ; Deqing Zou ; Yassin, Ali A.

  • Author_Institution
    Services Comput. Technol. & Syst. Lab. Cluster & Grid Comput. Lab., Huazhong Univ. of Sci. & Technol., Wuhan, China
  • fYear
    2014
  • fDate
    20-23 Dec. 2014
  • Firstpage
    3
  • Lastpage
    6
  • Abstract
    Cloud computing aids users to outsource their data in the cloud remotely to prevent them from burdens of local storage and maintenance. Users no longer have possession and control of these data. This property brings many new security challenges like unauthorised entities and correctness of stored data. In this paper, we focus on the problem of ensuring the integrity of data stored in the cloud. We propose a method which combines biometric and cryptography techniques in a cost-effective manner for data owners to gain trust in the cloud. We present efficient and secure integrity based on the XOR operation and iris feature extraction as the strong factors. This work gives the cloud user more confidence in detecting any block that has been changed. Additionally, our proposed scheme employs user´s iris features to secure and integrate data in a manner difficult for any internal or external entity to take or compromise it. Extensive security and performance analysis show that our scheme is highly efficient and provably secure.
  • Keywords
    cloud computing; cryptography; data integrity; feature extraction; iris recognition; XOR operation; biometric technique; cloud computing; cryptography technique; data integrity security; data outsourcing; external entity; internal entity; iris feature extraction; iris features; performance analysis; Cloud computing; Feature extraction; Iris; Iris recognition; Security; Servers;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Security Technology (SecTech), 2014 7th International Conference on
  • Conference_Location
    Haikou
  • Print_ISBN
    978-1-4799-7775-8
  • Type

    conf

  • DOI
    10.1109/SecTech.2014.8
  • Filename
    7023272