The limits of privacy in automated profiling and data mining

Computer Law and Security Review

Volumn 27, Issue 1, 2011, Pages 45-52

  Schermer, Bart W ^a  

^a Centre for Law in the Information Society   (United States)

Author keywords

Automated profiling; Data mining; Privacy

Indexed keywords

AUTOMATED PROFILING; DATA PROTECTION; INFORMATION ASYMMETRY; INFORMATION SOCIETY; POLICY OPTIONS; PRIVACY;

AUTOMATION; DATA PRIVACY;

DATA MINING;

EID: 79551552362     PISSN: 02673649     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.clsr.2010.11.009     Document Type: Article

References (35)

1. Hildebrandt, M. (2008), Defining Profiling: A New Type of Knowledge? In: Profiling the European Citizen, Cross-Disciplinary Perspectives (Hildebrandt, M., Gutwirth, S., eds.), Springer Science, p. 17.
2. Frawley, W.J., Piatetsky-Shapiro, G., Matheus, C.J. (1992), Knowledge Discovery in Databases: An Overview. In: AAAI Magazine, Fall 1992, p. 58.
3. See for instance: Marx, G.T. (1985), The surveillance society: the threat of 1984-style techniques. In: The Futurist, June 21-6;
4. Vedder, A. (1999), KDD: The challenge to individualism. In: Ethics and Information Technology, Volume 1, Number 4/December, 1999;
5. Lyon, D. (2003), Surveillance as social sorting, privacy risk and digital discrimination, New York: Routledge;
6. Custers, B. (2003), Effects of Unreliable Group Profiling by means of Data Mining. In: Lecture Notes in Computer Science, Volume 2843/2003, Springer;
7. and Hildebrandt, M., Gutwirth, S. (2008), Profiling the European Citizen, New York: Springer Verlag.
8. NWO Project Data mining without discrimination. For information see: http://www.onderzoekinformatie.nl/en/oi/nod/onderzoek/OND1337064/
9. Cocx (2009), Algorithmic tools for data-oriented law enforcement, PhD. Thesis, University of Leiden, p. 3.
10. Vedder, A. (1999), KDD: The challenge to individualism. In: Ethics and Information Technology, Volume 1, Number 4/December, 1999.
11. Custers, B.H.M. (2010), Data Mining with Discrimination Sensitive and Privacy Sensitive Attributes. Proceedings of ISP 2010, International Conference on Information Security and Privacy, 12-14 July 2010, Orlando, Florida (forthcoming).
12. Schermer, B.W. (2007), Software Agents, Surveillance, and the Right to Privacy: a Legislative Framework for Agent-enabled Surveillance, PhD. Thesis, Leiden University, p. 42.
13. Zarsky, T.Z. (2006), Online Privacy, Tailoring, and Persuasion. In: Privacy and technologies of Identity, a cross disciplinary conversation, Strandburg, K., Stan Raicu, D. (eds), Chapter 12, pp. 209-224, Springer, 2006.
14. Solove, D.J. (2004), The Digital Person: Technology and Privacy in the Information Age. New York: New York University Press, p. 47.
15. See for an in depth analysis of causation: Pearl, D. (2009), Causality: Models, Reasoning, and Inference, Cambridge: Cambridge University Press, (2nd edition).
16. Cocx, (2009), Algorithmic tools for data-oriented law enforcement, PhD. Thesis, University of Leiden, p. 143.
17. Zarsky, T.Z. (2002), Mine Your Own Business! Making the Case for the Implications of the Data Mining of Personal Information in the Forum of Public Opinion. In: Yale Journal of Law and Technology, pp. 1-57.
18. Verwer and Calders (2010), Three Naive Bayes Approaches for Discrimination-Free Classification. In: Data Mining: special issue with selected papers from ECMLePKDD 2010; Springer.
19. Pedreschi, D., Ruggieri, R., and Turini, F. (2008), Discriminationaware Data Mining. In: Proceedings of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;
20. Kamiran, F., Calders, T. (2009), Classification without discrimination, IEEE International Conference on Computer, Control & Communication (IEEE-IC4); IEEE press.
21. Schermer, B.W. (2007), Software Agents, Surveillance, and the Right to Privacy: a Legislative Framework for Agent-enabled Surveillance, PhD. Thesis, Leiden University, p. 198.
22. Schermer, B.W. (2007), Software Agents, Surveillance, and the Right to Privacy: a Legislative Framework for Agent-enabled Surveillance, PhD. Thesis, Leiden University, p. 115.
23. Weitzner et al. (2006), Transparent Accountable Data Mining: New Strategies for Privacy Protection, MIT Technical Report.
24. The recent retrial and subsequent acquittal of Dutch murder suspect Lucia de Berk after serving six years in prison highlights the impact faulty (interpretation of) statistics may have on a persons life.
25. COM (92) 422 finaleSYN 287, 15 October 1992, p. 26.
26. I use the term accountability here in the sense of answerability.
27. Fule, P., Roddick, J.F. (2006), Detecting privacy and ethical sensitivity in data mining results. In: Proceedings of the 27th Australasian conference on Computer science, Volume 26, pp. 159-166.
28. Schreuders, E. (2001), Data mining, de Toetsing van Beslisregels en Privacy, ITeR reeks 48 (in Dutch).
29. Ruggieri, S., Pedreschi, D., and Turini, F. (2010), Integrating induction and deduction for finding evidence of discrimination. In: Artificial Intelligence & Law, 18:1-43; Springer.
30. Bygrave, L. (2001), Minding the machine: article 15 of the EC data protection directive and automated profiling. In: Computer Law & Security Report, 2001, Volume 17, pp. 17-24.
31. Groothuis, M. (2003), Beschikken en digitaliseren: over normering van de elektronische overheid, PhD Thesis, Leiden University, p. 69 (in Dutch).
32. Lessig, L. (2006), Code 2.0, New York: Perseus Book Group.
33. Agrawal, R., Srikant, R. (2000), Privacy-Preserving Data Mining. In: ACM SIGMOD Record, Volume 29, Issue 2 (June 2000), pp. 439-450.
34. Kamiran, F., Calders, T. (2009), Classification without discrimination, IEEE International Conference on Computer, Control & Communication (IEEE-IC4); IEEE press.
35. Kamiran, F., Calders, T., Pecehnizkiy, M. (2009), Building Classifiers with Independency Constraints. In: IEEE ICDM Workshop on Domain Driven Data Mining. IEEE press 1.