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Securing data provenance in body area networks using lightweight wireless link fingerprints

Published: 11 November 2013 Publication History

Abstract

Wireless bodyworn sensing devices are becoming popular for fitness, sports training and personalized healthcare applications. In this paper, we demonstrate a mechanism to secure data provenance for these devices by exploiting symmetric spatio-temporal characteristics of the wireless link between two communicating parties. Our solution enables both parties to generate closely matching 'link' fingerprints which uniquely associate a data session with a wireless link such that a third party, at a later date, can verify the links the data was communicated on. These fingerprints are unique, they are very hard for an eavesdropper to forge, lightweight compared to traditional provenance mechanisms, and allow for certain interesting security properties such as system accountability and non-repudiation.

References

[1]
Nike+ FuelBand. Retrieved on 21 July, 2013. http://www.nike.com/us/en_us/c/nikeplus-fuelband.
[2]
Toumaz Technology Ltd. Sensium Life Platform. http://www.toumaz.com/page.php?page=sensium_intro.
[3]
B. Shebaro, S. Sultana, S. R. Gopavaram, and E. Bertino. Demonstrating a Lightweight Data Provenance for Sensor Networks. In ACM CCS, 2012.
[4]
S. Mathur, W. Trappe, N. Mandayam, C. Ye, and A. Reznik. Radio-telepathy: Extracting a Secret Key from an Unauthenticated Wireless Channel. In ACM MobiCom, 2008.
[5]
S. Jana, S. N. Premnath, M. Clark, S. K. Kasera, N. Patwari, and S. V. Krishnamurthy. On the Effectiveness of Secret Key Extraction from Wireless Signal Strength in Real Environments. In ACM MobiCom, 2009.

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  1. Securing data provenance in body area networks using lightweight wireless link fingerprints

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    cover image ACM Conferences
    SenSys '13: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
    November 2013
    443 pages
    ISBN:9781450320276
    DOI:10.1145/2517351
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 11 November 2013

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    Author Tags

    1. body area networks
    2. data provenance

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    SenSys '13 Paper Acceptance Rate 21 of 123 submissions, 17%;
    Overall Acceptance Rate 198 of 990 submissions, 20%

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