Towards statistically strong source anonymity for sensor networks

Min Shao, Yi Yang, Sencun Zhu, Guohong Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

153 Scopus citations

Abstract

For sensor networks deployed to monitor and report real events, event source anonymity is an attractive and critical security property, which unfortunately is also very difficult and expensive to achieve. This is not only because adversaries may attack against sensor source privacy through traffic analysis, but also because sensor networks are very limited in resources. As such, a practical tradeoff between security and performance is desirable. In this paper, for the first time we propose the notion of statistically strong source anonymity, under a challenging attack model where a global attacker is able to monitor the traffic in the entire network. We propose a scheme called FitProbRate, which realizes statistically strong source anonymity for sensor networks. We also demonstrate the robustness of our scheme under various statistical tests that might be employed by the attacker to detect real events. Our analysis and simulation results show that our scheme, besides providing source anonymity, can significantly reduce real event reporting latency compared to two baseline schemes.

Original languageEnglish (US)
Title of host publicationINFOCOM 2008
Subtitle of host publication27th IEEE Communications Society Conference on Computer Communications
Pages466-474
Number of pages9
DOIs
StatePublished - Sep 15 2008
EventINFOCOM 2008: 27th IEEE Communications Society Conference on Computer Communications - Phoenix, AZ, United States
Duration: Apr 13 2008Apr 18 2008

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X

Other

OtherINFOCOM 2008: 27th IEEE Communications Society Conference on Computer Communications
CountryUnited States
CityPhoenix, AZ
Period4/13/084/18/08

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Towards statistically strong source anonymity for sensor networks'. Together they form a unique fingerprint.

Cite this