Skip to main content
SpringerLink
Log in

Generating k-Anonymous Microdata by Fuzzy Possibilistic Clustering

  • Conference paper
  • First Online:
Database and Expert Systems Applications (DEXA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10439))

Included in the following conference series:

  • 1039 Accesses

Abstract

Collecting, releasing and sharing microdata about individuals is needed in some domains to support research initiatives aiming to create new valuable knowledge, by means of data mining and analysis tools. Thus, seeking individuals’ anonymity is required to guarantee their privacy prior publication. The k-anonymity by microaggregation, is a widely accepted model for data anonymization. It consists in de-associating the relationship between the identity of data subjects, i.e. individuals, and their confidential information. However, this method shows limits when dealing with real datasets. Indeed, the latter are characterized by their large number of attributes and the presence of noisy data. Thus, decreasing the information loss during the anonymization process is a compelling task to achieve. This paper aims to deal with such challenge. Doing so, we propose a microaggregation algorithm called Micro-PFSOM, based on fuzzy possibilitic clustering. The main thrust of this algorithm stands in applying an hybrid anonymization process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abidi, B., Yahia, S.B.: Multi-pfkcn: a fuzzy possibilistic clustering algorithm based on neural network. In: Proceedings of International Conference on Fuzzy Systems (FUZZ-IEEE 2013), Hyderabad, India, 7–10 July 2013, pp. 1–8. IEEE (2013)

    Google Scholar 

  2. Abidi, B., Yahia, S.B., Bouzeghoub, A.: A new algorithm for fuzzy clustering able to find the optimal number of clusters. In: Proceedings of 24th International Conference on Tools with Artificial Intelligence, ICTAI 2012, Athens, Greece, November 7–9 2012, pp. 806–813. IEEE (2012)

    Google Scholar 

  3. Aggarwal, C.C., Yu, P.S.: An introduction to privacy-preserving data mining. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy-Preserving Data Mining - Models and Algorithms. Advances in Database Systems, vol. 34, pp. 1–9. Springer, Boston (2008)

    Chapter  Google Scholar 

  4. Bacher, J., Brand, R., Bender, S.: Re-identifying register data by survey data using cluster analysis: an empirical study. Int. J. Uncertainty Fuzz. Knowl. Based Syst. 10(5), 589–607 (2002)

    Article  MATH  Google Scholar 

  5. Berkhin, P., Dhillon, I.S.: Knowledge discovery: clustering. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 5051–5064. Springer, New York (2009)

    Chapter  Google Scholar 

  6. Borgman, C.L.: The conundrum of sharing research data. J. Am. Soc. Inf. Sci. Technol. (JASIST) 63(6), 1059–1078 (2012)

    Article  Google Scholar 

  7. Chang, C.C., Li, Y.C., Huang, W.H.: Tfrp: An efficient microaggregation algorithm for statistical disclosure control. J. Syst. Softw. 80(11), 1866–1878 (2007)

    Article  Google Scholar 

  8. Dewri, R., Ray, I., Ray, I., Whitley, D.: On the optimal selection of k in the k-anonymity problem. In: Proceedings of the 24th International Conference on Data Engineering, ICDE 7–12 2008, Cancún, México, pp. 1364–1366. IEEE Computer Society, April 2008

    Google Scholar 

  9. Domigo-Ferrer, J., Solanas, A., Martínez-Ballesté, A.: Privacy in statistical databases: k-anonymity through microaggregation. In: Proceedings oh the IEEE International Conference on Granular Computing, GrC 2006, Atlanta, Georgia, USA, 10–12 May 2006, pp. 774–777 (2006)

    Google Scholar 

  10. Domingo-Ferrer, J., Martínez-Ballesté, A., Mateo-Sanz, J.M., Sebé, F.: Efficient multivariate data-oriented microaggregation. VLDB J. 15(4), 355–369 (2006)

    Article  Google Scholar 

  11. Domingo-Ferrer, J., Torra, V.: Disclosure risk assessment in statistical data protection. J. Comput. Appl. Math. 164–165(1), 285–293 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  12. Domingo-Ferrer, J., Torra, V.: Ordinal, continuous and heterogeneous k-anonymity through microaggregation. Data Min. Knowl. Disc. 11(2), 195–212 (2005)

    Article  MathSciNet  Google Scholar 

  13. Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). doi:10.1007/11787006_1

    Chapter  Google Scholar 

  14. Simson, L.: Garfinkel. De-identification of personal information. Technical report, National Institute of Standards and Technologie (2015)

    Google Scholar 

  15. Hu, W., Xie, D., Tan, T., Maybank, S.: Learning activity patterns using fuzzy self-organizing neural network. Syst. Man Cybern. Part B 34(3), 1618–1626 (2004)

    Article  Google Scholar 

  16. Ehrlich, R., Bezdek, J., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)

    Google Scholar 

  17. Kohonen, T., Schroeder, M.R., Huang, T.S.: Self-Organizing Maps, Chap. 3. Springer, Heidelberg (2001)

    Book  Google Scholar 

  18. Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1(2), 98–110 (1993)

    Article  Google Scholar 

  19. Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, The Marmara Hotel, Istanbul, Turkey, 15–20 April 2007, pp. 106–115. IEEE (2007)

    Google Scholar 

  20. Lin, J.L., Wen, T.H., Hsieh, J.C., Chang, P.C.: Density-based microaggregation for statistical disclosure control. Expert Syst. Appl. 37(4), 3256–3263 (2010)

    Article  Google Scholar 

  21. Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Disc. Data (TKDD) 1(1), 3 (2007)

    Article  Google Scholar 

  22. Oganian, A., Domingo-Ferrer, J.: On the complexity of optimal microaggregation for statistical disclosure control. Stat. J. United Nations Econ. Comission Eur. 18, 345–354 (2001)

    Google Scholar 

  23. Ohm, P.: Broken promises of privacy: responding to the surprising failure of anonymization. UCLA Law Rev. 57(6), 1701–1777 (2010)

    Google Scholar 

  24. Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.: A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst. 13(4), 517–530 (2005)

    Article  Google Scholar 

  25. Ramachandran, A., Singh, L., Porter, E., Nagle, F.: Exploring re-identification risks in public domains. In: Proceedings of the Tenth Annual International Conference on Privacy, Security and Trust, PST 2012, Paris, France, 16–18 July 2012, pp. 35–42. IEEE (2012)

    Google Scholar 

  26. Sweeney, L.: K-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzz. Knowl. Based Syst. 10(5), 557–570 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  27. Torra, V., Miyamoto, S.: Evaluating fuzzy clustering algorithms for microdata protection. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 175–186. Springer, Heidelberg (2004). doi:10.1007/978-3-540-25955-8_14

    Chapter  Google Scholar 

  28. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Sciences of Tunis, University of Tunis El Manar, LIPAH-LR 11ES14, 2092, Tunis, Tunisia

    Balkis Abidi & Sadok Ben Yahia

Authors
  1. Balkis Abidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sadok Ben Yahia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Balkis Abidi .

Editor information

Editors and Affiliations

  1. University of Lyon, Villeurbanne, France

    Djamal Benslimane

  2. University of Milan, Milan, Italy

    Ernesto Damiani

  3. University of Michigan, Dearborn, Michigan, USA

    William I. Grosky

  4. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  5. Wright State University, Dayton, Ohio, USA

    Amit Sheth

  6. Johannes Kepler University, Linz, Austria

    Roland R. Wagner

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Abidi, B., Ben Yahia, S. (2017). Generating k-Anonymous Microdata by Fuzzy Possibilistic Clustering. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10439. Springer, Cham. https://doi.org/10.1007/978-3-319-64471-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64471-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64470-7

  • Online ISBN: 978-3-319-64471-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation