Advertisement

On rough sets and inference analysis

  • Kan Zhang
Security Management
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1396)

Abstract

In this paper, we give an overview of a promising approach to inference detection and analysis in relational databases, first introduced in [25]. The approach employs techniques from rough sets theory and is able to take into account of all certain and possible material implications in the data, including functional dependencies. It can also be used to address inference threats posed by rule-induction techniques from data mining. A major advantage of this approach is that the quantitative measure IRI is computed directly from data without knowledge input from System Security Officer. By comparing with other techniques, we attempt to convey the merits of rough sets based approach.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    L. Bines, Inference through secondary path analysis, Proc. Sixth IFIP Working Conf. Database Security, Vancouver, B.C., Canada, Aug. 1992.Google Scholar
  2. 2.
    W. Buszkowski and E. Orlowska, On the Logic of Database Dependencies, Bulletin of Polish Academy of Sciences, Mathematics, Vol. 34, No. 5–6, 1986.Google Scholar
  3. 3.
    E.F. Codd, A Relational Model of Data for Large Shared Data Banks, Comm. ACM, Vol. 13, pp. 377–387, 1970.Google Scholar
  4. 4.
    H.S. Delugach and T.H. Hinke, Wizard: A Database Inference Analysis and Detection System, IEEE Trans. on Knowledge and Data Engineering, Vol. 8, No. 1, Feb 1996.Google Scholar
  5. 5.
    T.D. Garvey, T.F. Lunt, X. Qian, and M. Stickel, Toward a tool to detect and eliminate inference problems in the design of multilevel databases, Proc. Sixth IFIP Working Conf. Database Security, Vancouver, B.C., Canada, Aug. 1992.Google Scholar
  6. 6.
    T.D. Garvey, T.F. Lunt and M.E. Stickel, Abductive and Approximate Reasoning Models for Characterising Inference Channels, Proc. of the Computer Security Foundations Workshop IV, 1991.Google Scholar
  7. 7.
    J.W. Grzymala-Busse, Knowledge acquisition under uncertainty — A rough set approach, J. Intel. Rob. Syst., 1(1), pp 3–16, 1988.Google Scholar
  8. 8.
    J. Han, Y. Cai and N. Cercone, Knowledge Discrovery in Databases: An Attribute-Oriented Approach, Proc. 18th VLDB Conf., Vancouver, B.C., Canada, pp. 340–355, 1992.Google Scholar
  9. 9.
    X. Hu, N. Cercone and J. Han, An Attribute-Oriented Rough Set Approach for Knowledge Discovery in Databases, Proc. International Workshop on Rough Sets, Fuzzy Sets and Knowledge Discovery, Banff, Alberta, Canada, Oct., 1993.Google Scholar
  10. 10.
    T.H. Hinke and H.S. Delugach, AERIE: An Inference Modelling and Detection Approach for Databases, Proc. Sixth IFIP Working Conf. Database Security, Vancouver, B.C., Canada, Aug. 1992.Google Scholar
  11. 11.
    T.H. Hinke, Inference Aggregation Detection in Database Management Systems, Proc. 1988 IEEE Symposium on Security and Privacy, April 1988.Google Scholar
  12. 12.
    X. Hu, N. Shan, N. Cercone and W. Ziarko, DBROUGH: A Rough Set Based Knowledge Discovery System, Proc. 8th Intel Symp. on Methodologies for Intelligent Systems, Charlotte, NC., USA, 1994. (LNCS 869)Google Scholar
  13. 13.
    E. Krusinska, A Babic, R. Slowinski and J. Stefanowski, Comparison of the rough sets approach and probabilistic data analysis techniques on a common set of medical data, in Intelligent Decision Support, R. Slowinski, (ed.), Kluwer Academic Publishers, 1992.Google Scholar
  14. 14.
    T.Y. Lin, T.H. Hinke, D.G. Marks, and B. Thuraisingham, Security and Data Mining, Proc. Ninth IFIP Working Conf. Database Security, Aug. 1995.Google Scholar
  15. 15.
    D.G. Marks, Inference in MLS Database Systems, IEEE Trans. on Knowledge and Data Engineering, Vol. 8, No. 1, Feb 1996.Google Scholar
  16. 16.
    M. Morgenstern, Controlling Logical Inference in Multilevel Database Systems, Proc. 1988 IEEE Symposium on Security and Privacy, 1988.Google Scholar
  17. 17.
    Z. Pawlak, Rough Sets, In Theoretical Aspects of Reasoning About Data. Kluwer, Netherlands, 1991.Google Scholar
  18. 18.
    X. Qian, M.E. Stickel, P.D. Karp, T.F. Lunt, T.D. Garvey, Detection and Elimination of Inference Channels in Multilevel Relational Database Systems, Proc. 1993 IEEE Symposium on Security and Privacy, 1993.Google Scholar
  19. 19.
    S. Rath, D. Jones, J. Hale and S. Shenoi, A Tool for Inference Detection and Knowledge Discovery in Databases, Proc. Ninth IFIP Working Conf. Database Security, Aug. 1995.Google Scholar
  20. 20.
    R. Srikant and R. Agrawal, Mining Generalized Association Rules, Proc. of the 21st Int'l Conference on Very Large Databases, 1995.Google Scholar
  21. 21.
    R. Slowinski, J. Stefanowski: Rough classification in incomplete information systems, Mathematical and Computer Modelling 12 (1989) no.10/11, 1347–1357.Google Scholar
  22. 22.
    R. Slowinski, J. Stefanowski: Rough-Set Reasoning about Uncertain Data, Fundamenta Informaticae, 27(2/3): 229–243 (1996)Google Scholar
  23. 23.
    B. Thuraisingham, The Use of Conceptual Structures for Handling the Inference Problem, Proc. fifth IFIP Working Conf. Database Security, Shepherdstown, WV, November 1991.Google Scholar
  24. 24.
    J.D. Ullman, Principles of Database and Knowledge-Base Systems, vols. I and II, Rockville, MD.: Computer Science Press, 1988, 1989.Google Scholar
  25. 25.
    K. Zhang, IRI: A Quantitative Approach to Inference Analysis in Relational Databases, Proc. 11th IFIP Working Conf. Database Security, Lake Tahoe, CA, August 1997.Google Scholar
  26. 26.
    W. Ziarko, The Discovery, Analysis, and Representation of Data Dependencies in Databases, in Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W.J. Frawley, (eds) Menlo Park, CA: AAAI/MIT, 1991, 195–209.Google Scholar
  27. 27.
    W. Ziarko, Rough Sets and Knowledge Discovery: An Overview, Proc. International Workshop on Rough Sets, Fuzzy Sets and Knowledge Discovery, Banff, Alberta, Canada, Oct., 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Kan Zhang
    • 1
  1. 1.Cambridge University Computer LaboratoryCambridgeUK

Personalised recommendations