Anti-discrimination Analysis Using Privacy Attack Strategies

  • Salvatore Ruggieri
  • Sara Hajian
  • Faisal Kamiran
  • Xiangliang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8725)

Abstract

Social discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Frèchet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen, B.C., Kifer, D., Le Fevre, K., Machanavajjhala, A.: Privacy-preserving data publishing. Foundations and Trends in Databases 2(1-2), 1–167 (2009)CrossRefGoogle Scholar
  2. 2.
    Custers, B.H.M., Calders, T., Schermer, B.W., Zarsky, T.Z. (eds.): Discrimination and Privacy in the Information Society, Studies in Applied Philosophy, Epistemology and Rational Ethics, vol. 3. Springer (2013)Google Scholar
  3. 3.
    Dobra, A., Fienberg, S.E.: Bounds for cell entries in contingency tables given marginal totals and decomposable graphs. Proc. of the National Academy of Sciences 97(22), 11185–11192 (2000)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Domingo-Ferrer, J.: A survey of inference control methods for privacy-preserving data mining. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy-Preserving Data Mining. Advances in Database Systems, vol. 34, pp. 53–80. Springer (2008)Google Scholar
  5. 5.
    Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: A survey of recent developments. ACM Comput. Surv. 42(4), Article 14 (2010)Google Scholar
  6. 6.
    Hajian, S., Domingo-Ferrer, J.: A methodology for direct and indirect discrimination prevention in data mining. IEEE Trans. on Knowledge and Data Engineering 25(7), 1445–1459 (2013)CrossRefGoogle Scholar
  7. 7.
    Hajian, S., Domingo-Ferrer, J., Farràs, O.: Generalization-based privacy preservation and discrimination prevention in data publishing and mining. Data Mining and Knowledge Discovery, 1–31 (2014), doi:10.1007/s10618-014-0346-1Google Scholar
  8. 8.
    Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Nordholt, E.S., Spicer, K., de Wolf, P.P.: Statistical Disclosure Control. Wiley (2012)Google Scholar
  9. 9.
    Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems 33, 1–33 (2012)CrossRefGoogle Scholar
  10. 10.
    Kamiran, F., Karim, A., Zhang, X.: Decision theory for discrimination-aware classification. In: Proc. IEEE ICDM 2012, pp. 924–929 (2012)Google Scholar
  11. 11.
    Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-diversity: Privacy beyond k-anonymity. ACM Trans. on Knowledge Discovery from Data 1(1), Article 3 (2007)Google Scholar
  12. 12.
    Romei, A., Ruggieri, S.: A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review, 1–57 (2014), doi:10.1017/S0269888913000039Google Scholar
  13. 13.
    Ruggieri, S., Pedreschi, D., Turini, F.: Data mining for discrimination discovery. ACM Trans. on Knowledge Discovery from Data 4(2), Article 9 (2010)Google Scholar
  14. 14.
    Wong, R.C.W., Fu, A.W.C., Wang, K., Pei, J.: Minimality attack in privacy preserving data publishing. In: Proc. of VLDB 2007, pp. 543–554 (2007)Google Scholar
  15. 15.
    Xiao, X., Tao, Y.: Anatomy: Simple and effective privacy preservation. In: Proc. of VLDB 2006, pp. 139–150 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Salvatore Ruggieri
    • 1
  • Sara Hajian
    • 2
  • Faisal Kamiran
    • 3
  • Xiangliang Zhang
    • 4
  1. 1.Università di PisaItaly
  2. 2.Universitat Rovira i VirgiliSpain
  3. 3.Information TechnologyUniversity of the PunjabPakistan
  4. 4.King Abdullah University of Science and TechnologySaudi Arabia

Personalised recommendations