4th International ICST Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

Anonymous Data Collection in Sensor Networks

  • @INPROCEEDINGS{10.1109/MOBIQ.2007.4451016,
        author={James Horey and Michael M. Groat and Stephanie Forrest and Fernando Esponda},
        title={Anonymous Data Collection in Sensor Networks},
        proceedings={4th International ICST Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={IEEE},
        proceedings_a={MOBIQUITOUS},
        year={2008},
        month={2},
        keywords={Computer science  Histograms  Humans  Monitoring  Privacy  Protection  Protocols  Sensor phenomena and characterization  Telecommunication traffic  Traffic control},
        doi={10.1109/MOBIQ.2007.4451016}
    }
    
  • James Horey
    Michael M. Groat
    Stephanie Forrest
    Fernando Esponda
    Year: 2008
    Anonymous Data Collection in Sensor Networks
    MOBIQUITOUS
    IEEE
    DOI: 10.1109/MOBIQ.2007.4451016
James Horey1,*, Michael M. Groat1,*, Stephanie Forrest1,*, Fernando Esponda2,*
  • 1: Department of Computer Science University of New Mexico
  • 2: Department of Computer Science Yale University
*Contact email: jhorey@cs.unm.edu, mgroat@cs.unm.edu, forrest@cs.unm.edu, fesponda@cs.yale.edu

Abstract

Sensor networks involving human participants will require privacy protection before wide deployment is feasible. This paper proposes and evaluates a set of protocols that enable anonymous data collection in a sensor network. Sensor nodes, instead of transmitting their actual data, transmit a sample of the data complement to a basestation. The basestation then uses the negative samples to reconstruct a histogram of the original sensor readings. These protocols, collectively defined as a negative survey, are computationally simple and do not increase communication overhead. Thus, the negative survey can be implemented efficiently on existing sensor network platforms. We analyze the accuracy of the negative survey under a variety of conditions and define a range of parameter values for which it is practical. We also describe an example traffic monitoring application that uses the negative survey to classify traffic behavior. We demonstrate that for reasonable traffic scenarios, the system accurately classifies traffic behavior without revealing private information.