Key Generation Based on Acceleration Data of Shaking Processes

  • Daniel Bichler
  • Guido Stromberg
  • Mario Huemer
  • Manuel Löw
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4717)


Hard restrictions in computing power and energy consumption favour symmetric key methods to encrypt the communication in wireless body area networks which in term impose questions on effective and user-friendly unobtrusive ways for key distribution. In this paper, we present a novel approach to establish a secure connection between two devices by shaking them together. Instead of distributing or exchanging a key, the devices independently generate a key from the measured acceleration data by appropriate signal processing methods. Exhaustive practical experiments based on acceleration data gathered from real hardware prototypes have shown that in about 80% of the cases, a common key can be successfully generated. The average entropy of these generated keys exceed 13bits.


Activity Recognition Representation Vector Wireless Body Area Network Acceleration Data Acceleration Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Stajano, F.: Security for ubiquitous computing. John Wiley & Sons, Chichester (2002)Google Scholar
  2. 2.
    Cam, H., Özdemir, S., Muthuavinashiappan, D., Nair, P.: Energy-efficient security protocol for wireless sensor networks. In: IEEE VTC Fall Conference, vol. 5, pp. 2981–2984. IEEE, Los Alamitos (2003)Google Scholar
  3. 3.
    Bluetooth Spezial Interest Group: Specification of the Bluetooth System. Bluetooth, Version 1.1, vol. 1 (2001)Google Scholar
  4. 4.
    Bluetooth Spezial Interest Group: Specification of the Bluetooth System. Bluetooth, vol. 2, Version 1.1 (2001)Google Scholar
  5. 5.
    Muller, T.: Bluetooth security architecture. White Paper Version 1.0, Bluetooth (1999)Google Scholar
  6. 6.
    Diffie, W., Hellman, M.E.: New directions in cryptography. IEEE Transactions on Information Theory 22, 644–654 (1976)zbMATHCrossRefGoogle Scholar
  7. 7.
    Raymond, J.F., Stiglic, A.: Security issues in the Diffie-Hellman key agreement protocol. IEEE Transactions on Information Theory 22, 1–17 (2000)Google Scholar
  8. 8.
    Boyd, C., Mathuria, A.: Key establishment protocols for secure mobile communications: A selective survey. In: Boyd, C., Dawson, E. (eds.) ACISP 1998. LNCS, vol. 1438, pp. 3–540. Springer, Heidelberg (1998)Google Scholar
  9. 9.
    Bao, L.: Physical activity recognition from acceleration data under semi-naturalistic conditions. Master’s thesis, Massachusetts institute of technology (2003)Google Scholar
  10. 10.
    Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Hinkley, K.: Bumping objects together as a semantically rich way of forming connections between ubiquitous devices. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, Springer, Heidelberg (2003)Google Scholar
  12. 12.
    Lester, J., Hannaford, B., Borriello, G.: “Are you with me?” - Using accelerometers to determine if two devices are carried by the same person. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 33–50. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Holmquist, L.E., Mattern, F., Schiele, B., Alahuhta, P., Beigl, M., Gellersen, H.W.: Smart-its friends: A technique for users to easily establish connections between smart artefacts. In: Abowd, G.D., Brumitt, B., Shafer, S. (eds.) Ubicomp 2001: Ubiquitous Computing. LNCS, vol. 2201, pp. 273–291. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    Mayrhofer, R., Gellersen, H.: Shake well before use: Authentication based on acceleration data. In: Pervasive 2007: 5th International Conference on Pervasive Computing. LNCS, vol. 4480, pp. 144–161. Springer, Heidelberg (2007)Google Scholar
  15. 15.
    Huynh, T., Schiele, B.: Analyzing features for activity recognition. In: SocEUSAI, vol. 121, pp. 159–163 (2005)Google Scholar
  16. 16.
    Aylward, R., Lovell, S.D., Paradiso, J.A.: A compact, wireless, wearable sensor network for interactive dance ensembles. In: Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2006), vol. 00, pp. 65–70. IEEE Computer Society Press, Los Alamitos (2006)CrossRefGoogle Scholar
  17. 17.
    Shannon, C.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423, 623–656 (1948)Google Scholar
  18. 18.
    Weigend, A.S., Gershenfeld, N.A.: Time series prediction: Forecasting the future and understanding the past, vol. 15. Addison-Wesley Publishing Company (1994)Google Scholar
  19. 19.
    Molgedey, L., Ebeling, W.: Local order, entropy and predictability of financial time series. In: The European Physical Journal B - Condensed Matter and Complex Systems, vol. 15, pp. 733–737. Springer, Heidelberg (2004)Google Scholar
  20. 20.
    Proakis, J.G., Manolakis, D.G.: Digital Signal Processing: Principles, Algorithms, and Applications, 2nd edn. Macmillan Publishing Company (1992) ISBN 0-02-396815-XGoogle Scholar
  21. 21.
    Haykin, S.: Adaptive filter theory, 4th edn. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  22. 22.
    Patterson, D.: Artificial neural networks, theroy and applications. Prentice Hall Inc., Englewood Cliffs (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Daniel Bichler
    • 1
  • Guido Stromberg
    • 1
  • Mario Huemer
    • 2
  • Manuel Löw
    • 1
  1. 1.Infineon Technologies AGGermany
  2. 2.University of KlagenfurtAustria

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