Logica Universalis

, Volume 7, Issue 2, pp 195–209 | Cite as

A Universal Approach to Guarantee Data Privacy



The problem of data privacy is to verify that confidential information stored in an information system is not provided to unauthorized users and, therefore, personal and other sensitive data remain private. One way to guarantee this is to distort a knowledge base such that it does not reveal sensitive information. In the present paper we will give a universal definition of the problem of knowledge base distortion. It is universal in the sense that is independent of any particular knowledge representation formalism. We will then present a basic and general algorithm for knowledge base distortion to guarantee data privacy. This algorithm provides us with upper bounds for the complexity of the distortion problem. Moreover, we examine heuristics to improve its average performance.

Mathematics Subject Classification (2010)

03B70 68T27 


Data privacy controlled query evaluation inference control lying knowledge base systems propositional logic description logic 


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  1. 1.
    Agrawal, R., Kiernan, J., Srikant, R., Xu, Y.: Hippocratic databases. In: Proc. of 28th VLDB Conference (2002)Google Scholar
  2. 2.
    Baader, F., Brandt, S., Lutz, C.: Pushing the \({\mathcal{EL}}\) envelope. In: Kaelbling, L.P., Saffiotti, A. (eds.) IJCAI-05, Proceedings, pp. 364–369 (2005)Google Scholar
  3. 3.
    Biskup J., Bonatti P.A.: Controlled query evaluation for enforcing confidentiality in complete information systems. Int. J. Inf. Secur. 3(1), 14–27 (2004)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Biskup J., Bonatti P.A.: Controlled query evaluation for known policies by combining lying and refusal. Ann. Math. Artif. Intell. 40(1–2), 37–62 (2004)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Biskup J., Bonatti P.A.: Controlled query evaluation with open queries for a decidable relational submodel. Ann. Math. Artif. Intell. 50(1–2), 39–77 (2007)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Biskup, J., Weibert, T.: Keeping secrets in incomplete databases. Int. J. Inf. Sec. 7(3) (2008)Google Scholar
  7. 7.
    Biskup J., Wiese L.: Preprocessing for controlled query evaluation with availability policy. J. Comput. Secur. 16(4), 477–494 (2008)Google Scholar
  8. 8.
    Bonatti P.A., Kraus S., Subrahmanian V.S.: Foundations of secure deductive databases. Trans. Knowl. Data Eng. 7(3), 406–422 (1995)CrossRefGoogle Scholar
  9. 9.
    Bovet, D.P. Crescenzi, P.: Introduction to the theory of complexity. Prentice Hall (1994)Google Scholar
  10. 10.
    Bundesamt für Statistik. Medizinische Statistik der KrankenhäuserGoogle Scholar
  11. 11.
    Calvanese D., Giacomo G., Lembo D., Lenzerini M., Rosati R.: Tractable reasoning and efficient query answering in description logics: The DL-Lite family. J. Autom. Reason. 39(3), 385–429 (2007)MATHCrossRefGoogle Scholar
  12. 12.
    Horrocks, I., Sattler, U., Tobies, S.: Practical reasoning for very expressive description logics. Logic J. IGPL 8(3) (2000)Google Scholar
  13. 13.
    Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information (abstract). In: PODS, p. 188 (1998)Google Scholar
  14. 14.
    Schlobach, S., Cornet, R.: Non-standard reasoning services for the debugging of description logic terminologies. In: Gottlob, G., Walsh, T. (eds.) IJCAI, pp. 355–362 (2003)Google Scholar
  15. 15.
    Sicherman G.L., De Jonge W., Van de Riet R.P.: Answering queries without revealing secrets. ACM Trans. Database Syst. 8(1), 41–59 (1983)MATHCrossRefGoogle Scholar
  16. 16.
    Stoffel, K., Studer, T.: Provable data privacy. In: Viborg, K., Debenham, J., Wagner, R. (eds.) DEXA 2005, LNCS, vol. 3588, pp. 324–332, Springer (2005)Google Scholar

Copyright information

© Springer Basel AG 2012

Authors and Affiliations

  1. 1.Institut für Informatik und angewandte MathematikUniversität BernBernSwitzerland

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