Temporal Context Lie Detection and Generation

  • Xiangdong An
  • Dawn Jutla
  • Nick Cercone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4165)


In pervasive (ubiquitous) environments, context-aware agents are used to obtain, understand, and share local contexts with each other so that all resources in the environments could be integrated seamlessly. Context exchanging should be made privacy-conscious, which is generally controlled by users’ privacy preferences. Besides who has rights to get what true information about him, a user’s privacy preference could also designate who should be given obfuscated information. By obfuscation, people could present their private information in a coarser granularity, or simply in a falsified manner, depending on the specific situations. Nevertheless, obfuscation cannot be done randomly because by reasoning the receiver could know the information has been obfuscated. An obfuscated context can not only be inferred from its dependencies with other existing contexts, but could also be derived from its dependencies with the vanished ones. In this paper, we present a dynamic Bayesian network (DBN)-based method to reason about the obfuscated contexts in pervasive environments, where the impacts of the vanished historical contexts are properly evaluated. On the one hand, it can be used to detect obfuscations, and may further find the true information; on the other hand, it can help reasonably obfuscate information.


Privacy management context inference inference control obfuscation pervasive computing dynamic Bayesian networks uncertain reasoning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abowd, G.D., Dey, A., Orr, R., Bortherton, J.: Context-awareness in wearable and ubiquitous computing. Virtual Reality 3(3), 200–211 (1998)CrossRefGoogle Scholar
  2. 2.
    Davies, N., Gellersen, H.W.: Beyond prototypes: Challenges in deploying ubiquitous systems. IEEE Pervasive Computing 1(1), 26–35 (2002)CrossRefGoogle Scholar
  3. 3.
    Khedr, M., Karmouch, A.: Exploiting agents and SIP for smart context level agreements. In: Proceedings of IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing, Victoria, BC, Canada, pp. 1000–1003 (2003)Google Scholar
  4. 4.
    Gandon, F.L., Sadeh, N.M.: Semantic web technologies to reconcile privacy and context awareness. Journal of Web Semantics 1(3) (2005)Google Scholar
  5. 5.
    Khedr, M., Karmouch, A.: Negotiating context information in context-aware systems. IEEE Intelligent Systems 19(6), 21–29 (2004)CrossRefGoogle Scholar
  6. 6.
    Chen, H., Finin, T., Joshi, A.: An ontology for context-aware pervasive computing environments. Knowledge Engineering Review, Special Issue on Ontologies for Distributed Systems 18(3), 197–207 (2004)Google Scholar
  7. 7.
    Dey, A.: Understanding and using context. Personal and Ubiquitous Computing 5(1), 4–7 (2001)CrossRefGoogle Scholar
  8. 8.
    Chou, S.C., Hsieh, W.T., Gandon, F.L., Sadeh, N.M.: Semantic web technologies for context-aware museum tour guide applications. In: Proceedings of the 19th International Conference on Advanced Information Networking and Applications (AINA 2005), vol. 2, pp. 709–714 (2005)Google Scholar
  9. 9.
    Khedr, M., Karmouch, A.: ACAI: Agent-based context-aware infrastructure for spontaneous applications. Journal of Network and Computer Applications 28(1), 19–44 (2005)CrossRefGoogle Scholar
  10. 10.
    Westin, A.F.: Privacy and Freedom. Atheneum, New York (1967)Google Scholar
  11. 11.
    Hull, R., Kumar, B., Lieuwen, D., Patel-Schneider, P.F.: Enabling context-aware and privacy-conscious user data sharing. In: Proceedings of the 2004 IEEE International Conference on Mobile Data Management (MDM 2004), pp. 103–109 (2004)Google Scholar
  12. 12.
    Cranor, L., Langheinrich, M., Marchiori, M., Presler-Marshall, M., Reagle, J.: The platform for privacy preferences 1.0 (P3P 1.0) specification. Technical report, W3C Recommendation (2002), http://www.w3.org/TR/P3P
  13. 13.
    Biskup, J., Bonatti, P.A.: Lying versus refusal for known potential secrets. Data & Knowledge Engineering 38, 199–222 (2001)MATHCrossRefGoogle Scholar
  14. 14.
    Duckham, M., Kulik, L.: A formal model of obfuscation and negotiation for location privacy. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 152–170. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Biskup, J.: For unknown secrecies refusal is better than lying. Data & Knowledge Engineering 33, 1–24 (2000)MATHCrossRefGoogle Scholar
  16. 16.
    Denning, D.E., Schlörer, J.: Inference control for statistical databases. IEEE Computer 16(7), 69–82 (1983)Google Scholar
  17. 17.
    Dey, A., Mankoff, J., Abowd, G., Carter, S.: Distributed mediation of ambiguous context in aware environments. In: Beaudouin-Lafon, M. (ed.) Proceedings of the 15th Annual ACM Symposium on User Interface Software and Technology (UIST 2002), Paris, France, pp. 121–130. ACM Press, New York (2002)CrossRefGoogle Scholar
  18. 18.
    Gu, T., Peng, H.K., Zhang, D.Q.: A Bayesian approach for dealing with uncertain contexts. In: Proceedings of the Second International Conference on Pervasive Computing (Pervasive 2004), Austrian Computer Society, Vienna (2004)Google Scholar
  19. 19.
    Neapolitan, R.E.: Probabilistic Reasoning in Expert Systems: Theory and Algorithms. John Wiley & Sons, Inc, New York (1990)Google Scholar
  20. 20.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Franciso (1988)Google Scholar
  21. 21.
    Haddawy, P.: An overview of some recent developments in Bayesian problem solving techniques. AI Magazine 20(2), 11–19 (1999)Google Scholar
  22. 22.
    Dean, T., Kanazawa, K.: Probabilistic temporal reasoning. In: Proceedings of the 7th National Conference on Artificial Intelligence (AAAI-1988), St. Paul, Minnesota, pp. 524–528. AAAI Press, Menlo Park (1988)Google Scholar
  23. 23.
    Dagum, P., Galper, A., Horvitz, E., Seiver, A.: Uncertain reasoning and forescasting. International Journal of Forecasting 11(1), 73–87 (1995)CrossRefGoogle Scholar
  24. 24.
    Nicholson, A.E., Brady, J.M.: Dynamic belief networks for discrete monitoring. IEEE Transactions on Systems, Man, and Cybernetics, special issue on Knowledge-Based Construction of Probabilistic and Decision Models 24(11), 1593–1610 (1994)Google Scholar
  25. 25.
    Li, X., Ji, Q.: Active affective state detection and user assistance with dynamic Bayesian networks. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 35(1), 93–105 (2005)CrossRefMathSciNetGoogle Scholar
  26. 26.
    Oliver, N., Horvitz, E.: A comparison of hMMs and dynamic bayesian networks for recognizing office activities. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 199–209. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  27. 27.
    Dagum, P., Galper, A., Horvitz, E.: Dynamic network models for forecasting. In: Dubois, D., Wellman, M.P., D’Ambrosio, B., Smets, P. (eds.) Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence (UAI-1992), Stanford, CA, USA, pp. 41–48. Morgan Kaufmann, San Francisco (1992)Google Scholar
  28. 28.
    Nefian, A.V., Liang, L., Pi, X., Murphy, K.: Dynamic Bayesian networks for audio-visual speech recognition. EURASIP Journal on Applied Signal Processing 11, 1–15 (2002)Google Scholar
  29. 29.
    Hanks, S., Madigan, D., Gavrin, J.: Probabilistic temporal reasoning with endogenous change. In: Besnard, P., Hanks, S. (eds.) Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (UAI-1995), Montréal, Québec, Canada. Morgan Kaufmann Publishers, San Francisco (1995)Google Scholar
  30. 30.
    Salem, A.B., Bouillaut, L., Aknin, P., Weber, P.: Dynamic Bayesian networks for classification of rail defects. In: Proceedings of the Fourth International Conference on Intelligent Systems Design and Applications (ISDA 2004), Budapest, Hungary (2004)Google Scholar
  31. 31.
    Biskup, J., Bonatti, P.: Controlled query evaluation for known policies by combing lying and refusal. Annals of Mathematics and Artificial Intelligence 40(1-2), 37–62 (2004)MATHCrossRefMathSciNetGoogle Scholar
  32. 32.
    Dorndorf, U., Pesch, E., Phan-Huy, T.: Constraint propagation techniques for disjunctive scheduling problems. Artificial Intelligence 122, 189–240 (2000)MATHCrossRefMathSciNetGoogle Scholar
  33. 33.
    Cook, S.A.: The complexity of theorem-proving procedure. In: Harrison, M.A., Banerji, R.B., Ullman, J.D. (eds.) Proceedings of the 3rd Annual ACM Symposium on Theorey of Computing (STOC 1971), Shaker Heights, OH, pp. 151–158. ACM Press, New York (1971)CrossRefGoogle Scholar
  34. 34.
    Jajodia, S., Sandhu, R.: Polyinstantiation integrity in multilevel relations. In: Proceedings of the 1990 IEEE Computer Symposium on Research in Security and Privacy, Oakland, CA, pp. 104–115. IEEE Computer Society, Los Alamitos (1990)CrossRefGoogle Scholar
  35. 35.
    Cuppens, F., Gabillon, A.: Logical foundations of multilevel databases. Data & Knowledge Engineering 29(3), 199–222 (1999)CrossRefGoogle Scholar
  36. 36.
    Yip, R.W., Levitt, K.N.: Data level inference detection in database systems. In: Proceedings of the 11th IEEE Computer Security Foundations, Rockport, MA, pp. 179–189 (1998)Google Scholar
  37. 37.
    Staddon, J.: Dynamic inference control. In: Zaki, M.J., Aggarwal, C.C. (eds.) Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD 2003), San Diego, CA, pp. 94–100. ACM Press, New York (2003)CrossRefGoogle Scholar
  38. 38.
    Fellegi, I.: On the question fo statistical confidentiality. Journal of American Statistical Association 67(337), 7–18 (1972)MATHCrossRefGoogle Scholar
  39. 39.
    Denning, D.E., Denning, P.J., Schwartz, M.D.: The tracker: a threat to statistical database security. ACM Transactions on Database Systems 4(1), 76–96 (1979)CrossRefGoogle Scholar
  40. 40.
    Dobkin, D., Jones, A., Lipton, R.: Secure databases: Protection against user influence. ACM Transactions on Database Systems 4(1), 97–106 (1979)CrossRefGoogle Scholar
  41. 41.
    Cox, L.H.: Suppression methodology and statistical disclosure control. Journal of the American Statistical Association 75(370), 377–385 (1980)MATHCrossRefGoogle Scholar
  42. 42.
    Bayardo, R.J., Agrawal, R.: Data privacy through optimal k-anonymization. In: Proceedings of the 21st International Conference on Data Engineering (ICDE 2005), Tokyo, Japan, pp. 217–228. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar
  43. 43.
    Narayanan, A., Shmatikov, V.: Obfuscated databases and group privacy. In: Atluri, V., Meadows, C., Juels, A. (eds.) Proceedings of the 12th ACM Conference on Computer and Communications Security (CCS 2005), Alexandria, VA, USA, pp. 102–111. ACM Press, New York (2005)CrossRefGoogle Scholar
  44. 44.
    Chin, F.Y., Özsoyoglu, G.: Auditing and inference control in statistical databases. IEEE Transactions on Software Engineering 8(6), 574–582 (1982)CrossRefGoogle Scholar
  45. 45.
    Kleinberg, J., Papadimitriou, C., Raghavan, P.: Auditing boolean attributes. In: Proceedings of the 19th ACM SIGMOD-SIGART Symposium on Principles of Database Systems (PODS 2000), Dallas, TX, pp. 86–91. ACM Press, New York (2000)CrossRefGoogle Scholar
  46. 46.
    Traub, J.F., Yemini, Y., Woznaikowski, H.: The statistical security of a statistical database. ACM Transactions on Database Systems 9(4), 672–679 (1984)CrossRefGoogle Scholar
  47. 47.
    Beck, L.L.: A security mechanism for statistical databases. ACM Transactions on Database Systems 5(3), 316–338 (1980)MATHCrossRefGoogle Scholar
  48. 48.
    Reiss, S.P.: Practical data-swapping: The first steps. ACM Transactions on Database Systems 9(1), 20–37 (1984)MATHCrossRefGoogle Scholar
  49. 49.
    Denning, D.: Secure statistical databases with random sample queries. ACM Transactions on Database Systems 5(3), 291–315 (1980)MATHCrossRefGoogle Scholar
  50. 50.
    Díaz, C., Seys, S., Claessens, J., Preneel, B.: Towards measuring anonymity. In: Dingledine, R., Syverson, P.F. (eds.) PET 2002. LNCS, vol. 2482, pp. 54–68. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  51. 51.
    Steinbrecher, S., Köpsell, S.: Modelling unlinkability. In: Dingledine, R. (ed.) PET 2003. LNCS, vol. 2760, pp. 32–47. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  52. 52.
    Mazhelis, O., Puuronen, S., Veijalainen, J.: Modelling dependencies between classifiers in mobile masquerader detection. In: López, J., Qing, S., Okamoto, E. (eds.) ICICS 2004. LNCS, vol. 3269, pp. 318–330. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  53. 53.
    Mazhelis, O., Puuronen, S.: Combining one-class classifiers for mobile-user substitution detection. In: Proceedings of the 6th International Conference on Enterprise Information Systems (ICEIS 2004), Porto, Portugal, pp. 32–47 (2004)Google Scholar
  54. 54.
    Ghahramani, Z.: Learning dynamic bayesian networks. In: Giles, C.L., Gori, M. (eds.) IIASS-EMFCSC-School 1997. LNCS (LNAI), vol. 1387, pp. 168–197. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  55. 55.
    Friedman, N., Murphy, K., Russell, S.: Learning the structure of dynamic probabilistic networks. In: Cooper, G.F., Moral, S. (eds.) Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-1998), Madison, WI, USA. Morgan Kaufmann Publishers, San Francisco (1998)Google Scholar
  56. 56.
    Boyen, X.: Inference and Learning in Complex Stochastic Processes. PhD thesis, Computer Science Department. Stanford University, Stanford, CA, USA (2002)Google Scholar
  57. 57.
    Peña, J.M., Björkegren, J., Tegnèr, J.: Learning dynamic Bayesian network models via cross-validation. Pattern Recognition Letters 26(14), 2295–2308 (2005)CrossRefGoogle Scholar
  58. 58.
    Dojer, N., Gambin, A., Mizera, A., Wilczynski, B., Tiuryn, J.: Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinformatics 7 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiangdong An
    • 1
    • 2
  • Dawn Jutla
    • 2
  • Nick Cercone
    • 1
  1. 1.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada
  2. 2.Finance and Management Science DepartmentSaint Mary’s UniversityHalifaxCanada

Personalised recommendations