Applying Soft Computing Technologies for Implementing Privacy-Aware Systems

  • Christos Kalloniatis
  • Petros Belsis
  • Evangelia Kavakli
  • Stefanos Gritzalis
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 112)


Designing privacy-aware systems gains much attention in recent years. One of the main issues for the protection of users’ privacy is the proper selection and realization of the respective Privacy Enhancing Technologies for the realization of the privacy requirements identified in the design phase. The selection of PETs must be conducted in a way that best fits the organization’s needs as well as other organization’s criteria like cost, complexity etc. In this paper the PriS method, which is used for incorporating security and privacy requirements early in the system development process, is extended by combining knowledge from a soft computing approach in order to improve the way that respective PETs are selected for the realization of the respective requirements incorporated during the design phase.


Organizational Goal Requirement Engineer Evidence Theory Privacy Requirement PriS Method 
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  1. 1.
    Kalloniatis, C., Kavakli, E., Gritzalis, S.: Methods for Designing Privacy Aware Information Systems: A review. In: Alexandris, N., Chryssikopoulos, V., Douligeris, C., Kanellopoulos, N. (eds.) Proceedings of the PCI 2009 13th Pan-Hellenic Conference on Informatics (with International Participation). IEEE CPS Conference Publishing Services, Corfu (2009)Google Scholar
  2. 2.
    Loucopoulos, P., Kavakli, V.: Enterprise Knowledge Management and Conceptual Modelling. In: Chen, P.P., Akoka, J., Kangassalu, H., Thalheim, B. (eds.) Conceptual Modeling. LNCS, vol. 1565, pp. 123–143. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  3. 3.
    Loucopoulos, P.: From Information Modelling to Enterprise Modelling. In: Information Systems Engineering: State of the Art and Research Themes, pp. 67–78. Springer, Berlin (2000)Google Scholar
  4. 4.
    Kavakli, E., Gritzalis, S., Kalloniatis, C.: Protecting Privacy in System Design: The Electronic Voting Case. Transforming Government People Process and Policy 1(4), 307–332 (2007)CrossRefGoogle Scholar
  5. 5.
    Kalloniatis, C., Kavakli, E., Gritzalis, S.: Addressing privacy requirements in system design: The PriS method. Requirements Engineering 13(3), 241–255 (2008)CrossRefGoogle Scholar
  6. 6.
    Kalloniatis, C., Kavakli, E., Kontellis, E.: PRIS tool: A case tool for privacy-oriented Requirements Engineering. Journal of Information Systems Security 6(1), 3–19 (2010); AIS SIGSECGoogle Scholar
  7. 7.
    University of the Aegean, E-Vote: An Internet-based electronic voting system. University of the Aegean, Project Deliverable D 7.6, IST Programme 2000#29518, Samos (November 21, 2003)Google Scholar
  8. 8.
    Kavakli, E., Kalloniatis, C., Loucopoulos, P., Gritzalis, S.: Incorporating Privacy Requirements into the System Design Process: The PriS Conceptual Framework. Internet Research, Special issue on Privacy and Anonymity in the Digital Era: Theory, Technologies and Practice 16(2), 140–158 (2006)Google Scholar
  9. 9.
    Kalloniats, C., Kavakli, E., Gritzalis, S.: Dealing with Privacy Issues during the System Design Process. In: 5th IEEE International Symposium on Signal Processing and Information Technology, Athens, Greece, December 18–21 (2005)Google Scholar
  10. 10.
    Kavakli, V.: Goal Oriented Requirements Engineering: A Unifying Framework. Requirements Engineering Journal 6(4), 237–251 (2002)MATHCrossRefGoogle Scholar
  11. 11.
    Kalloniatis, C., Kavakli, E., Gritzalis, S.: PriS Methodology: Incorporating Privacy Requirements into the System Design Process. In: Mylopoulos, J., Spafford, G. (eds.) Proceedings of the SREIS 2005 13th IEEE International Requirements Engineering Conference – Symposium on Requirements Engineering for Information Security. IEEE CPS, Paris (2005)Google Scholar
  12. 12.
    Klir, G., Yuan, B.: Fuzzy sets and fuzzy logic. Prentice-Hall (1995)Google Scholar
  13. 13.
    Zadeh, L.A.: Review of Books: A Mathematical Theory of Evidence. The AI Magazine 5(3), 81–83 (1984)Google Scholar
  14. 14.
    Yager, R.: Quasi-Associative Operations in the Combination of Evidence. Kybernetes 16, 37–41 (1987)MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Sentz, K., Ferson, S.: Combination of Evidence in Dempster-Shafer Theory, Sandia National Laboratories, Technical Report SAND 2002-0835, Albuquerque, New Mexico (2002)Google Scholar
  16. 16.
    Inagaki, T.: Interdependence between Safety-Control Policy and Multiple-Sensor, Schemes Via Dempster-Shafer Theory. IEEE Transactions on Reliability 40(2), 182–188 (1991)MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christos Kalloniatis
    • 1
  • Petros Belsis
    • 2
  • Evangelia Kavakli
    • 1
  • Stefanos Gritzalis
    • 3
  1. 1.Department of Cultural Technology and CommunicationUniversity of the AegeanMytileneGreece
  2. 2.Department of MarketingTechnological Education Institute of AthensAthensGreece
  3. 3.Information and Communication Systems Security Laboratory, Department of Information and Communications Systems EngineeringUniversity of the AegeanSamosGreece

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