Formalizing DIKW Architecture for Modeling Security and Privacy as Typed Resources

  • Yucong Duan
  • Lougao Zhan
  • Xinyue Zhang
  • Yuanyuan ZhangEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 270)


Currently the content of security protection has been expanded multiple sources. The security protection especially of the implicit content from multiple sources poses new challenges to the collection, identification, customization of protection strategies, modeling, etc. We are enlightened by the potential of DIKW (Data, Information, Knowledge, Wisdom) architecture to express semantic of natural language content and human intention. But currently there lacks formalized semantics for the DIKW architecture by itself which poses a challenge for building conceptual models on top of this architecture. We proposed a formalization of the elements of DIKW. The formalization centers the ideology of modeling Data as multiple dimensional hierarchical Types related to observable existence of the Sameness, Information as identification of Data with explicit Difference, Knowledge as applying Completeness of the Type, and Wisdom as variability prediction. Based on this formalization, we propose a solution framework for security concerns centering Type transitions in Graph, Information Graph and Knowledge Graph.


Typed resources Data, Information, Knowledge, Wisdom 



We acknowledge Hainan Project No. ZDYF2017128, NSFC under Grant (No. 61363007, No. 61662021, and No. 61502294), Zhejiang Province medical and health science and technology platform project No. 2017KY497.*refers correspondence.


  1. 1.
    Appelt, D.E.: FASTUS: a finite-state processor for information extraction from real-world text. In: Proceedings of IJCAI 1993, pp. 1172–1178 (1993)Google Scholar
  2. 2.
    Pearson, P.D., Hansen, J., Gordon, C.J.: The effect of background knowledge on young children’s comprehension of explicit and implicit information. J. Literacy Res. 11(3), 201–209 (1979)Google Scholar
  3. 3.
    Chen, M., et al.: Data, information, and knowledge in visualization. IEEE Comput. Graph. Appl. 29(1), 12–19 (2008)CrossRefGoogle Scholar
  4. 4.
    Zins, C.: Conceptual approaches for defining data, information, and knowledge. J. Assoc. Inf. Sci. Technol. 58(4), 479–493 (2014)CrossRefGoogle Scholar
  5. 5.
    Duan, Y., Fu, X., Hu, Q., Gu, Y.: An ontology definition framework for model driven development. In: Gavrilova, M.L., et al. (eds.) ICCSA 2006. LNCS, vol. 3983, pp. 746–755. Springer, Heidelberg (2006). Scholar
  6. 6.
    Grishman, R.: Information extraction: techniques and challenges. In: Pazienza, M.T. (ed.) SCIE 1997. LNCS, vol. 1299, pp. 10–27. Springer, Heidelberg (1997). Scholar
  7. 7.
    Hundepool, A., et al.: Statistical Disclosure Control. Wiley, New York (2012)CrossRefGoogle Scholar
  8. 8.
    Kant, I.: Critique of Pure Reason. Cambridge University Press, Cambridge (1998)CrossRefGoogle Scholar
  9. 9.
    Shao, L., Duan, Y., Cui, L., Zou, Q., Sun, X.: A pay as you use resource security provision approach based on data graph, information graph and knowledge graph. In: Yin, H., et al. (eds.) IDEAL 2017. LNCS, vol. 10585, pp. 444–451. Springer, Cham (2017). Scholar
  10. 10.
    Frank, D.: McSherry: privacy integrated queries: an extensible platform for privacy-preserving data analysis. Commun. ACM 53(9), 89–97 (2010)CrossRefGoogle Scholar
  11. 11.
    Schopenhauer, A.: The world as will and representation. Kenyon Rev. 20(3/4), 44–46 (1998)Google Scholar
  12. 12.
    Soria-Comas, J., Domingo-Ferrer, J.: Big data privacy: challenges to privacy principles and models. Data Sci. Eng. 1(1), 21–28 (2016)CrossRefGoogle Scholar
  13. 13.
    Duan, Y., Shao, L., Hu, G., Zhou, Z., Zou, Q., Lin, Z.: Specifying architecture of knowledge graph with data graph, information graph, knowledge graph and wisdom graph. In: International Conference on Software Engineering Research, pp. 327–332. IEEE (2017)Google Scholar
  14. 14.
    Aamodt, A., et al.: Different roles and mutual dependencies of data, information, and knowledge—an AI perspective on their integration. Data Know. Eng. 16(3), 191–222 (1995)CrossRefGoogle Scholar
  15. 15.
    Tarr, P., Ossher, H., Harrison, W., Sutton Jr., S.M.: N degrees of separation: multi-dimensional separation of concerns. In: International Conference on Software Engineering, vol. 3, pp. 107–119. IEEE (1999)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Yucong Duan
    • 1
  • Lougao Zhan
    • 1
  • Xinyue Zhang
    • 1
  • Yuanyuan Zhang
    • 2
    Email author
  1. 1.College of Information Science and TechnologyHainan UniversityHaikouChina
  2. 2.College of Information TechnologyZhejiang Chinese Medical UniversityHangzhouChina

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