Intelligent Methods for Cyber Warfare pp 87-114

Part of the Studies in Computational Intelligence book series (SCI, volume 563) | Cite as

Microfiles as a Potential Source of Confidential Information Leakage

Chapter

Abstract

Cyber warfares, as well as conventional ones, do not only comprise direct military conflicts involving weapons like DDoS attacks. Throughout their history, intelligence and counterintelligence played a major role as well. Information sources for intelligence can be closed (obtained during espionage) or open. In this chapter, we show that such open information sources as microfiles can be considered a potentially important additional source of information during cyber warfare. We illustrate by using real data based example that ignoring issues concerning providing group anonymity can lead to leakage of confidential information. We show that it is possible to define fuzzy groups of respondents and obtain their distribution using appropriate fuzzy inference system. We conclude the chapter with discussing methods for protecting distributions of crisp as well as fuzzy groups of respondents, and illustrate them by solving the task of providing group anonymity of a fuzzy group of “respondents who can be considered military enlisted members with the high level of confidence.”

References

  1. 1.
    Gantz, J., Reinsel, D.: Big data, bigger digital shadows, and biggest growth in the Far East. http://www.emc.com/leadership/digital-universe/iview/executive-summary-a-universe-of.htm (2012)
  2. 2.
    Pfitzmann, A., Hansen, M.: A terminology for talking about privacy by data minimization: anonymity, unlinkability, undetectability, unobservability, pseudonymity, and identity management, Version v0.34, http://dud.inf.tu-dresden.de/Anon_Terminology.shtml (2010)
  3. 3.
    Chertov, O., Tavrov, D.: Data group anonymity: general approach. Int. J. Comput. Sci. Inf. Secur. 8(7), 1–8 (2010)Google Scholar
  4. 4.
    Chertov, O. (ed.): Group Methods of Data Processing. Lulu.com, Raleigh (2010)Google Scholar
  5. 5.
    Sweeney, L.: Computational Disclosure Control: A Primer on Data Privacy. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge (2001)Google Scholar
  6. 6.
    Evfimievski, A.: Randomization in privacy preserving data mining. ACM SIGKDD Explor. Newslett. 4(2), 43–48 (2002)CrossRefGoogle Scholar
  7. 7.
    Domingo-Ferrer, J., Mateo-Sanz, J.M.: Practical data-oriented microaggregation for statistical disclosure control. IEEE Trans. Knowl. Data Eng. 14(1), 189–201 (2002)CrossRefGoogle Scholar
  8. 8.
    Fienberg, S.E., McIntyre, J.: Data swapping: variations on a theme by Dalenius and Reiss. In: Domingo-Ferrer, J., Torra, V. (eds.) Privacy in Statistical Databases, PSD 2004. LNCS, vol. 3050, pp. 14–29. Springer, Berlin (2004)Google Scholar
  9. 9.
    Wang, J., Zhong, W., Zhang, J.: NNMF-based factorization techniques for high-accuracy privacy protection on non-negative-valued datasets. The 6th IEEE International Conference on Data Mining Workshops. ICDM Workshops 2006, Hong Kong, December 2006, pp. 513–517. IEEE Computer Society Press, Washington (2006)Google Scholar
  10. 10.
    Xu, S., Zhang, J., Han, D., Wang, J.: Singular value decomposition based data distortion strategy for privacy protection. Knowl. Inf. Syst. 10(3), 383–397 (2006)CrossRefGoogle Scholar
  11. 11.
    Liu, L., Wang, J., Zhang, J.: Wavelet-based data perturbation for simultaneous privacy-preserving and statistics-preserving. In: 2008 IEEE International Conference on Data Mining Workshops, Pisa, December 2008, pp. 27–35. IEEE Computer Society Press (2008)Google Scholar
  12. 12.
    National Institute of Statistics and Economic Studies. Minnesota Population Center. Integrated Public Use Microdata Series, International: Version 6.2 [Machine-readable database]. University of Minnesota, Minneapolis, https://international.ipums.org/international/ (2013)
  13. 13.
    Nuclear Power in France, World Nuclear Association, http://www.world-nuclear.org/info/inf40.html
  14. 14.
    Chertov, O., Tavrov, D.: Group anonymity. In: Hllermeier, E., Kruse, R., Hoffmann, F. (eds.) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. CCIS, vol. 81, pp. 592–601. Springer, Berlin (2010)Google Scholar
  15. 15.
    Chertov, O., Tavrov, D.: Group anonymity: problems and solutions. Lviv Polytechnic Natl. Univ. J. Info. Syst. Netw. 673, 3–15 (2010)Google Scholar
  16. 16.
    Chertov, O., Tavrov, D.: Providing data group anonymity using concentration differences. Mathe. Mach. Syst. 3, 34–44 (2010)Google Scholar
  17. 17.
    Tishchenko, V., Mladientsev, M.: Dmitrii Ivanovich Miendielieiev, yego zhizn i dieiatielnost. Univiersitietskii pieriod 1861–1890 gg. Nauka, Moskva (1993) (In Russian)Google Scholar
  18. 18.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci. 8, 199–249 (1975)CrossRefMATHMathSciNetGoogle Scholar
  19. 19.
    Chertov, O., Tavrov, D.: Providing Group Anonymity Using Wavelet Transform. In: MacKinnon, L.M. (ed.) Data Security and Security Data. LNCS, vol. 6121, pp. 25–36. Springer, Berlin (2012)CrossRefGoogle Scholar
  20. 20.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7(1), 1–13 (1975)CrossRefMATHGoogle Scholar
  21. 21.
    Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, Upper Saddle River (1995)Google Scholar
  22. 22.
    Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. SMC-3(1), 28–44 (1973)Google Scholar
  23. 23.
    U. S. Census 2000. 5-Percent Public Use Microdata Sample Files, http://www.census.gov/main/www/cen2000.html
  24. 24.
    Demographics. Profile of the Military Community. Office of the Deputy under Secretary of Defense (Military Community and Family Policy), http://www.militaryonesource.mil/12038/MOS/Reports/2011_Demographics_Report.pdf (2012)
  25. 25.
    Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, New York (1999)MATHGoogle Scholar
  26. 26.
    Chertov, O.R.: Minimizatsiia spotvoren pry formuvanni mikrofailu z zamaskovanymy danymy. Visnyk Skhid-noukrainskoho Natsionalnoho Universytetu imeni Volodymyra Dalia, 8(179), 256–262 (2012) (In Ukrainian)Google Scholar
  27. 27.
    Daubechies, I.: Ten lectures on wavelets. Soc. Ind. Appl. Math. (1992)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.National Technical University of Ukraine, Kyiv Polytechnic InstituteKyivUkraine

Personalised recommendations