Skip to main content
SpringerLink
Log in

An Analysis of Different Notions of Effectiveness in k-Anonymity

  • Conference paper
  • First Online:
Privacy in Statistical Databases (PSD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12276))

Included in the following conference series:

Abstract

k-anonymity is an approach for enabling privacy-preserving data publishing of personal, sensitive data. As a result of this anonymisation process, the utility of the sanitised data is generally lower than on the original data. Quantifying this utility loss is therefore important to estimate the usefulness of the resulting datasets. In this paper, we analyse several of these utility aspects.

Data utility can be measured as a direct property of the resulting, anonymised dataset, or via the effectiveness that a statistical analysis, such as a machine learning model, achieves upon this dataset, as compared to the original data. While the latter is more tailored to the specific dataset, it is also generally less efficient. We therefore analyse whether there is a correlation between these two types of measures, and whether the measurement on the effectiveness can be substituted by a measurement of the data properties. Further, we evaluate to what extent different solutions for the same level of k-anonymity differ in regards to effectiveness.

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 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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

Notes

  1. 1.

    https://arx.deidentifier.org/.

  2. 2.

    https://arx.deidentifier.org/overview/metrics-for-information-loss/.

  3. 3.

    https://archive.ics.uci.edu/ml/datasets/Adult.

  4. 4.

    https://scikit-learn.org/stable/index.html.

  5. 5.

    https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.pearsonr.html.

References

  1. 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). https://doi.org/10.1007/11787006_1

    Chapter  Google Scholar 

  2. Hittmeir, M., Ekelhart, A., Mayer, R.: On the utility of synthetic data: an empirical evaluation on machine learning tasks. In: International Conference on Availability, Reliability and Security (ARES), Canterbury, UK. ACM (2019)

    Google Scholar 

  3. Chen, B.-C., Kifer, D., LeFevre, K., Machanavajjhala, A.: Privacy-preserving data publishing. Found. Trends Databases 2(1–2), 1–167 (2009)

    Article  Google Scholar 

  4. Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression (1998)

    Google Scholar 

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

    Google Scholar 

  6. Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: L-diversity: privacy beyond k-anonymity. In: 22nd International Conference on Data Engineering (ICDE 2006) (2006)

    Google Scholar 

  7. Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: 2007 IEEE 23rd International Conference on Data Engineering, Istanbul. IEEE (2007)

    Google Scholar 

  8. Samarati, P.: Protecting respondents identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)

    Article  Google Scholar 

  9. Kohlmayer, F., Prasser, F., Eckert, C., Kemper, A., Kuhn, K.A.: Flash: efficient, stable and optimal k-anonymity. In: International Conference on Privacy, Security, Risk and Trust and International Confernce on Social Computing, Amsterdam, Netherlands. IEEE (2012)

    Google Scholar 

  10. Campan, A., Truta, T.M.: Data and structural k-anonymity in social networks. In: Bonchi, F., Ferrari, E., Jiang, W., Malin, B. (eds.) PInKDD 2008. LNCS, vol. 5456, pp. 33–54. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01718-6_4

    Chapter  Google Scholar 

  11. Malle, B., Kieseberg, P., Holzinger, A.: DO NOT DISTURB? Classifier behavior on perturbed datasets. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2017. LNCS, vol. 10410, pp. 155–173. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66808-6_11

    Chapter  Google Scholar 

  12. Jordi, S.-C., Josep, D.-F., David, S., Sergio, M.: t-closeness through microaggregation: strict privacy with enhanced utility preservation. IEEE Trans. Knowl. Data Eng. 27(11), 3098–3110 (2015)

    Article  Google Scholar 

  13. Fabian, P., Raffael, B., Kuhn, K.A.: A generic method for assessing the quality of de-identified health data. Stud. Health Technol. Inform. 228, 312–316 (2016)

    Google Scholar 

  14. Iyengar, V.S.: Transforming data to satisfy privacy constraints. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Edmonton, Alberta, Canada. ACM Press (2002)

    Google Scholar 

  15. Wimmer, H., Powell, L.: A comparison of the effects of k-anonymity on machine learning algorithms. In: Proceedings of the Conference for Information Systems Applied Research (2014)

    Google Scholar 

  16. Abdul, M., Farman, U., Lee, S.: Vulnerability- and diversity-aware anonymization of personally identifiable information for improving user privacy and utility of publishing data. Sensors 17(5), 1059 (2017)

    Article  Google Scholar 

  17. LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Mondrian multidimensional k-anonymity. In: 22nd International Conference on Data Engineering (ICDE 2006), Atlanta, GA, USA. IEEE (2006)

    Google Scholar 

  18. Malle, B., Kieseberg, P., Weippl, E., Holzinger, A.: The right to be forgotten: towards machine learning on perturbed knowledge bases. In: Buccafurri, F., Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-ARES 2016. LNCS, vol. 9817, pp. 251–266. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45507-5_17

    Chapter  Google Scholar 

Download references

Acknowledgements

This work was partially funded by the BRIDGE 1 programme (No 871267, “WellFort”) of the Austrian Research Promotion Agency (FFG), the EU Horizon 2020 research and innovation programme under grant agreement No. 826078 (Project “FeatureCloud”). SBA Research (SBA-K1) is funded within the framework of COMET—Competence Centers for Excellent Technologies by BMVIT, BMDW, and the federal state of Vienna, managed by the FFG.

Author information

Authors and Affiliations

  1. SBA Research, Vienna, Austria

    Tanja Šarčević & Rudolf Mayer

  2. Vienna University of Technology, Vienna, Austria

    David Molnar & Rudolf Mayer

Authors
  1. Tanja Šarčević
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Molnar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rudolf Mayer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rudolf Mayer .

Editor information

Editors and Affiliations

  1. Rovira i Virgili University, Tarragona, Catalonia, Spain

    Josep Domingo-Ferrer

  2. University of Oklahoma, Norman, OK, USA

    Krishnamurty Muralidhar

Appendix

Appendix

Fig. 8.
figure 8

Classification results of the best, middle and worst found dataset of the solution space

Full size image
Fig. 9.
figure 9

Rankings for education

Full size image
Fig. 10.
figure 10

Rankings for marital-status

Full size image
Fig. 11.
figure 11

Utility metrics and loss correlation for education

Full size image
Fig. 12.
figure 12

Utility metrics and loss correlation for marital-status

Full size image

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Šarčević, T., Molnar, D., Mayer, R. (2020). An Analysis of Different Notions of Effectiveness in k-Anonymity. In: Domingo-Ferrer, J., Muralidhar, K. (eds) Privacy in Statistical Databases. PSD 2020. Lecture Notes in Computer Science(), vol 12276. Springer, Cham. https://doi.org/10.1007/978-3-030-57521-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-57521-2_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57520-5

  • Online ISBN: 978-3-030-57521-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation