• DocumentCode
    2073742
  • Title

    Proximity-based security using ambient radio signals

  • Author

    Liang Xiao ; Qiben Yan ; Wenjing Lou ; Hou, Y.T.

  • Author_Institution
    Dept. Commun. Eng., Xiamen Univ., Xiamen, China
  • fYear
    2013
  • fDate
    9-13 June 2013
  • Firstpage
    1609
  • Lastpage
    1613
  • Abstract
    In this paper, we propose a privacy-preserving proximity-based security strategy for location-based services in wireless networks, without requiring any pre-shared secret, trusted authority or public key infrastructure. More specifically, radio clients build their location tags according to the unique physical features of their ambient radio signals, which cannot be forged by attackers outside the proximity range. The proximity-based authentication and session key generation is based on the public location tag, which incorporates the received signal strength indicator (RSSI), sequence number and MAC address of the ambient radio packets. Meanwhile, as the basis for the session key generation, the secret location tag consisting of the arrival time interval of the ambient packets, is never broadcast, making it robust against eavesdroppers and spoofers. The proximity test utilizes the nonparametric Bayesian method called infinite Gaussian mixture model, and provides range control by selecting different features of various ambient radio sources. The authentication accuracy and key generation rate are evaluated via experiments using laptops in typical indoor environments.
  • Keywords
    Bayes methods; Gaussian processes; data privacy; message authentication; public key cryptography; radio networks; MAC address; RSSI; ambient radio signals; infinite Gaussian mixture model; location-based services; nonparametric Bayesian method; privacy-preserving proximity-based security strategy; proximity test; proximity-based authentication; public key infrastructure; public location tag; received signal strength indicator; secret location tag; sequence number; session key generation; wireless networks; Authentication; Bayes methods; Gaussian mixture model; Monitoring; Tin;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications (ICC), 2013 IEEE International Conference on
  • Conference_Location
    Budapest
  • ISSN
    1550-3607
  • Type

    conf

  • DOI
    10.1109/ICC.2013.6654745
  • Filename
    6654745