From Data Minimization to Data Minimummization

  • Bart van der Sloot
Part of the Studies in Applied Philosophy, Epistemology and Rational Ethics book series (SAPERE, volume 3)


Data mining and profiling offer great opportunities, but also involve risks related to privacy and discrimination. Both problems are often addressed by implementing data minimization principles, which entail restrictions on gathering, processing and using data. Although data minimization can sometimes help to minimize the scale of damage that may take place in relation to privacy and discrimination, for example when a data leak occurs or when data are being misused, it has several disadvantages as well. Firstly, the dataset loses a rather large part of its value when personal and sensitive data are filtered from it. Secondly, by deleting these data, the context in which the data were gathered and had a certain meaning is lost. This chapter will argue that this loss of contextuality, which is inherent to data mining as such but is aggravated by the use of data minimization principles, gives rise to or aggravates already existing privacy and discrimination problems. Thus, an opposite approach is suggested, namely that of data minimummization, which requires a minimum set of data being gathered, stored and clustered when used in practice. This chapter argues that if the data minimummization principle is not realized, this may lead to quite some inconveniences; on the other hand, if the principle is realized, new techniques can be developed that rely on the context of the data, which may provide for innovative solutions. However, this is far from a solved problem and it requires further research.


Data Mining Knowledge Discovery Personal Data Sensitive Data Data Minimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Bu, S., et al.: Preservation of Patterns and Input-Output Privacy. In: Proceedings of ICDE 2007, pp. 696–705 (2007)Google Scholar
  2. Calders, T., Verwer, S.: Three Naive Bayes Approaches for Discrimination-Free Classification. Data Mining and Knowledge Discovery 21(2), 277–292 (2010)MathSciNetCrossRefGoogle Scholar
  3. Custers, B.H.M.: The Power of Knowledge; Ethical, Legal, and Technological Aspects of Data Mining and Group Profiling in Epidemiology. Wolf Legal Publishers, Tilburg (2004)Google Scholar
  4. Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy preserving mining of association rules. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2002), pp. 217–228 (2002)Google Scholar
  5. Fulda, J.S.: Data Mining and Privacy. Alb. L.J. Sci. & Tech. (11), 105–113 (2000)Google Scholar
  6. Grice, H.P.: Logic and conversation. In: Cole, P., Morgan, J. (eds.) Syntax and Semantics, vol. (3), pp. 41–58. Academic Press, New York (1975)Google Scholar
  7. Guzik, K.: Discrimination by Design: Data Mining in the United States’s ‘War on Terrorism’. Surveillance & Society (7), 1–17 (2009)Google Scholar
  8. Hildebrandt, M., Gutwirth, S. (eds.): Profiling the European Citizen Cross-Disciplinary Perspectives. Springer, New York (2008)Google Scholar
  9. Kantarcioglu, M., Jin, J., Clifton, C.: When do data mining results violate privacy? In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD 2004), pp. 599–604. ACM, New York (2004)CrossRefGoogle Scholar
  10. Kuhn, P.: Sex discrimination in labor markets: The role of statistical evidence. The American Economic Review (77), 567–583 (1987)Google Scholar
  11. LaCour-Little, M.: Discrimination in mortgage lending: A critical review of the literature. Journal of Real Estate Literature (7), 15–50 (1999)Google Scholar
  12. Larose, D.T.: Data mining methods and models. John Wiley & Sons, Inc. All, New Yersey (2006)zbMATHGoogle Scholar
  13. Müller, V.C.: Would you mind being watched by machines? Privacy concerns in data mining. AI & Soc. (23), 529–544 (2009)Google Scholar
  14. Pedreschi, D., Ruggieri, S., Turini, F.: Discrimination-aware Data Mining. In: KDD, pp. 560–568 (2008)Google Scholar
  15. Porter, C.C.: De-Identified Data and Third Party Data Mining: The Risk of Re-Identification of Personal Information. Shidler i.L. Com. & Tech. (30) article no. 3 (2008)Google Scholar
  16. Ramasastry, A.: Lost in translation? Data mining, national security and the “adverse inference” problem. Santa Clara Computer & High Tech. L. J. (22), 757–796 (2006)Google Scholar
  17. Renke, W.N.: Who controls the past now controls the future: counter-terrorism, data mining and privacy. Alta. L. Rev. (43), 779–823 (2006)Google Scholar
  18. Ruggieri, S., Pedreschi, D., Turini, F.,: Data Mining for Discrimination Discovery. Transactions on Knowledge Discovery from Data 4(2), 9:1-9:40 (2010)Google Scholar
  19. Schermer, B.W.: The limits of privacy in automated profiling and data mining. Computer Law & Security Review 2(7), 45–52 (2011)CrossRefGoogle Scholar
  20. Skillicorn, D.: Knowledge Discovery for Counterterrorism and Law Enforcement. Taylor & Francis Group, LLC, Boca Raton (2009)Google Scholar
  21. Squires, G.D.: Racial profiling, insurance style: Insurance redlining and the uneven development of metropolitan areas. Journal of Urban Affairs 25(4), 391–410 (2003)MathSciNetCrossRefGoogle Scholar
  22. Tavani, H.T.: Genomic research and data-mining technology: Implications for personal privacy and informed consent. Ethics and Information Technology (6), 15–28 (2004)Google Scholar
  23. Vermeulen, P.: Autisme als Context Blindheid. EPO, Berchem (2009)Google Scholar
  24. Verykios, V.S., et al.: State-of-the-art in Privacy Preserving Data Mining. Sigmod Record 33(1), 50–57 (2004)CrossRefGoogle Scholar
  25. Wang, T., Liu, L.: Output Privacy in Data Mining. Transactions on Database Systems 36(1), 1–37 (2011)CrossRefGoogle Scholar
  26. Westphal, C.: Data mining for Intelligence, Fraud & Criminal Detection. Taylor & Francis Group, LLC, Boca Raton (2009)Google Scholar
  27. Working Party, Opinion 4/2007 on the concept of personal data. WP 136: 01248/07/EN (2007)Google Scholar
  28. Zarsky, T.Z.: Mini your own business!: making the case for the implications of the data mining of personal information in the forum of public opinion. Yale Journal of Law & Technology (5), 1–56 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute for Information LawUniversity of AmsterdamAmsterdamThe Netherlands

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