Shadow: A Middleware in Pervasive Computing Environment for User Controllable Privacy Protection

  • Wentian Lu
  • Jun Li
  • Xianping Tao
  • Xiaoxing Ma
  • Jian Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4272)


In ubiquitous and pervasive computing, after data owner’s information is collected, data collector should be careful of disclosing data owner’s information for privacy reasons. In this paper, we present requirements and challenges when designing solutions for such data collector end protection. Policies, accuracy and anonymity of context should be all taken into account. Based on this, we design a middleware Shadow for user controllable privacy protection, which is deployed on data collectors who have large volume of data and powerful computation abilities. Shadow has a contextual rule based access control policy mechanism, enriched with methods of generating blurred context and guaranteeing information anonymous, and we implement it under an ontology based context model.


Data Collector Data User Pervasive Computing Context Data Data Owner 
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.
    Harper, R.H.R.: Why people do and don?t wear active badges: A case study. Computer Supported Cooperative Work 4(4), 297–318 (1995)CrossRefGoogle Scholar
  2. 2.
    Li, J., Bu, Y., Chen, S., Tao, X., Lu, J.: Followme: On research of pluggable infrastructure for context-awareness. In: AINA, vol. (1), pp. 199–204. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  3. 3.
    Snekkenes, E.: Concepts for personal location privacy policies. In: ACM Conference on Electronic Commerce, pp. 48–57. ACM, New York (2001)CrossRefGoogle Scholar
  4. 4.
    Myles, G., Friday, A., Davies, N.: Preserving Privacy in Environments with Location-Based Applications. IEEE Pervasive Computing 2(1), 56–64 (2003)CrossRefGoogle Scholar
  5. 5.
    Hengartner, U., Steenkiste, P.: Protecting access to people location information. In: Hutter, D., Müller, G., Stephan, W., Ullmann, M. (eds.) Security in Pervasive Computing. LNCS, vol. 2802, pp. 25–38. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Reiter, M.K., Rubin, A.D.: Crowds: Anonymity for web transactions. ACM Trans. Inf. Syst. Secur. 1(1), 66–92 (1998)CrossRefGoogle Scholar
  7. 7.
    Goldschlag, D.M., Reed, M.G., Syverson, P.F.: Onion routing. Commun. ACM 42(2), 39–41 (1999)CrossRefGoogle Scholar
  8. 8.
    Beresford, A.R., Stajano, F.: Location Privacy in Pervasive Computing. IEEE Pervasive Computing 2(1), 46–55 (2003)CrossRefGoogle Scholar
  9. 9.
    Heiber, T., Marron, P.J.: Exploring the relationship between context and privacy. In: Robinson, P., Vogt, H., Wagealla, W. (eds.) Privacy, Security and Trust within the Context of Pervasive Computing. The Kluwer International Series in Engineering and Computer Science, vol. 780 (2005); University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology. Springer-Verlag, ISBN 0-387-23461-6Google Scholar
  10. 10.
    Sweene, L.: k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(5), 557–570 (2002)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Wang, K., Yu, P.S., Chakraborty, S.: Bottom-up generalization: A data mining solution to privacy protection. In: ICDM, pp. 249–256. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  12. 12.
    Sweeney, L.: Datafly: A system for providing anonymity in medical data. In: Lin, T.Y., Qian, S. (eds.) DBSec. IFIP Conference Proceedings, vol. 113, pp. 356–381. Chapman & Hall, Boca Raton (1997)Google Scholar
  13. 13.
    Langheinrich, M.: Privacy by design - principles of privacy-aware ubiquitous systems. In: Abowd, G.D., Brumitt, B., Shafer, S. (eds.) UbiComp 2001. LNCS, vol. 2201, pp. 273–291. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    Langheinrich, M.: A privacy awareness system for ubiquitous computing environments. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, pp. 237–245. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Gandon, F.L., Sadeh, N.M.: Semantic web technologies to reconcile privacy and context awareness. J. Web Sem. 1(3), 241–260 (2004)Google Scholar
  16. 16.
    Hong, J.I., Landay, J.A.: An architecture for privacy-sensitive ubiquitous computing. In: MobiSys, USENIX (2004)Google Scholar
  17. 17.
    Zugenmaier, A., Kreuzer, M., Müller, G.: The freiburg privacy diamond: An attacker model for a mobile computing environment. In: Irmscher, K., Fähnrich, K.P. (eds.) KiVS Kurzbeiträge, pp. 131–141. VDE Verlag (2003)Google Scholar
  18. 18.
    Sweene, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(5), 571–588 (2002)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Gruteser, M., Grunwald, D.: Anonymous usage of location-based services through spatial and temporal cloaking. In: MobiSys, USENIX (2003)Google Scholar
  20. 20.
    Tang, K.P., Keyani, P., Fogarty, J., Hong, J.I.: Putting people in their place: an anonymous and privacy-sensitive approach to collecting sensed data in locationbased applications. In: CHI 2006: Proceedings of the SIGCHI conference on Human Factors in computing systems, pp. 93–102. ACM Press, New York (2006)CrossRefGoogle Scholar
  21. 21.
    Anciaux, N., van Heerde, H., Feng, L., Apers, P.: Implanting Life-Cycle Privacy Policies in a Context Database. Technical Report TR-CTIT-06-03, CTIT, University of Twente (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wentian Lu
    • 1
  • Jun Li
    • 1
  • Xianping Tao
    • 1
  • Xiaoxing Ma
    • 1
  • Jian Lu
    • 1
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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