Using SAT-Solvers to Compute Inference-Proof Database Instances

  • Cornelia Tadros
  • Lena Wiese
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5939)


An inference-proof database instance is a published, secure view of an input instance containing secret information with respect to a security policy and a user profile. In this paper, we show how the problem of generating an inference-proof database instance can be represented by the partial maximum satisfiability problem. We present a prototypical implementation that relies on highly efficient SAT-solving technology and study its performance in a number of test cases.


Patient Type Soft Constraint Hard Constraint Conjunctive Normal Form Propositional Variable 
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.
    Argelich, J., Li, C.M., Manyà, F., Planes, J.: MaxSAT evaluation,
  2. 2.
    Biskup, J., Bonatti, P.A.: Controlled query evaluation with open queries for a decidable relational submodel. Annals of Mathematics and Artificial Intelligence 50(1-2), 39–77 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Biskup, J., Wiese, L.: Preprocessing for controlled query evaluation with availability policy. Journal of Computer Security 16(4), 477–494 (2008)Google Scholar
  4. 4.
    Biskup, J., Wiese, L.: Combining consistency and confidentiality requirements in first-order databases. In: Samarati, P., Yung, M., Martivelli, F., Andagna, C.A. (eds.) ISC 2009. LNCS, vol. 5735, pp. 121–134. Springer, Heidelberg (2009)Google Scholar
  5. 5.
    Cuppens, F., Gabillon, A.: Cover story management. Data & Knowledge Engineering 37(2), 177–201 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Heras, F., Larrosa, J., de Givry, S., Schiex, T.: 2006 and 2007 Max-SAT Evaluations: Contributed Instances. Journal on Satisfiability, Boolean Modeling and Computation 4(1), 239–250 (2008)zbMATHGoogle Scholar
  7. 7.
    Heras, F., Larrosa, J., Oliveras, A.: MiniMaxSAT: An efficient Weighted Max-SAT Solver. Journal of Artificial Intelligence Research 31, 1–32 (2008)zbMATHMathSciNetGoogle Scholar
  8. 8.
    Jukic, N., Nestorov, S., Vrbsky, S.V., Parrish, A.S.: Enhancing database access control by facilitating non-key related cover stories. Journal of Database Management 16(3), 1–20 (2005)Google Scholar
  9. 9.
    Larrosa, J., Heras, F., de Givry, S.: A logical approach to efficient Max-SAT solving. Artificial Intelligence 172(2-3), 204–233 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Sutcliffe, G.: TPTP, TSTP, CASC, etc. In: Diekert, V., Volkov, M.V., Voronkov, A. (eds.) CSR 2007. LNCS, vol. 4649, pp. 6–22. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Toland, T.S., Farkas, C., Eastman, C.M.: Dynamic disclosure monitor (D\(^{\mbox{{2}}}\)Mon): An improved query processing solution. In: Jonker, W., Petković, M. (eds.) SDM 2005. LNCS, vol. 3674, pp. 124–142. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Cornelia Tadros
    • 1
  • Lena Wiese
    • 1
  1. 1.Technische Universität DortmundDortmundGermany

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