Enhanced Source Location Privacy Based on Random Perturbations for Wireless Sensor Networks
DOI:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Source location privacy, which means to protect source sensors' locations from being leaked out of observed network tra±c, is an emerging research topic in wireless sensor networks, because it cannot be fully addressed by traditional cryptographic mechanisms, such as encryption and authentication. Current source location privacy schemes, assuming either a local or global attack model, have limitations. For example, schemes under a global attack model are subject to a so called `01' attack, during which an attacker can potentially identify sources of real messages. Targeting on tackling this attack, we propose two perturbation schemes, one based on Uniform Distribution and the other based on Gaussian Distribution. We analyze the security properties of these two schemes. We also simulate and compare them with previous schemes, with results showing that the proposed perturbation schemes can improve sensor source location privacy significantly. Furthermore, it is realized that an attacker may employ more intelligent statistical tools, such as Univariate Distribution based Reconstruction (UDR), to analyze the traffic generation patterns and find out real sources. We propose a Risk Region (RR) based technique, to prevent the attacker from successfully doing this. Performance evaluation shows that the RR-based scheme increases the errors of the attacker, so that the attacker is not able to accurately derive real messages as well as their sources.

    Reference
    Related
    Cited by
Get Citation

Uthaiwan Srimongkolpitak, Yi Yang, Hang Liu. Enhanced Source Location Privacy Based on Random Perturbations for Wireless Sensor Networks. International Journal of Software and Informatics, 2014,8(2):145~166

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:
  • Revised:
  • Adopted:
  • Online: January 30,2015
  • Published: