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Big Data—Fighting Organized Crime Threats While Preserving Privacy

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Using Open Data to Detect Organized Crime Threats

Abstract

This chapter deals with the challenge of balancing privacy and national security in the context of big data -driven sense-making systems for crime fighting , which aim at improving law enforcement agencies opportunities for strategic, proactive planning in response to emerging organized crime threats, specifically by employing environmental scanning. It is stressed that democratic societies are faced with the challenge of striking a balance between two sides of security, formulated as absence of organized crime threats and preservation of the freedom and integrity of the individual as important presumptions for democracy. Consequently, it is argued that crime fighting technologies ought to be designed in a way that balance data utility and data privacy and hence ensure that informational harm will not occur, which might otherwise endanger citizens’ trust in law enforcement authorities and undermine police legitimacy.

The chapter is an elaborated version of a conference short paper: Gerdes. A (2015) EPOOLICE Security Technology—Fighting Organized Crime Whilst Balancing Privacy and National Security, which was presented at the 10th International Conference on Cyber Warfare and Security, March 24–25, 2015, South Africa, Krüger National Park. I am grateful for valuable comments from the conference audience.

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References

  • Anderson, C. (2008). wired.com. The end of theory: The data deluge makes the scientific method obsolete. http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory. Accessed February 8, 2017.

  • Ansoff, H. I. (1975). Managing strategic surprise by response to weak signals. California Management Review, XVIII(2), 21–33.

    Article  Google Scholar 

  • Barboro, M., & Zeller, T. (2016). A face exposed for AOL searcher no. 4417749. The New York Times. http://www.nytimes.com/2006/08/09/technology/09aol.html?pagewanted=all. Accessed February 8, 2017.

  • Barocas, S., & Nissenbaum, H. (2014). Big data’s end run around anonymity and consent. In J. Lane, V. Stodden, S. Bender (Eds.), Privacy, big data, and the public good (pp. 44–75). New York: Cambridge University Press.

    Google Scholar 

  • Benn, S. (1971). Privacy, freedom and respect for persons. In J. R. Pennock & J. W. Chapman (Eds.), Privacy (pp. 1–27). New York: Atherton Press.

    Google Scholar 

  • Benn, S. I. (1988). A theory of freedom. New York: Cambridge University Press.

    Google Scholar 

  • Biehn, N. (2013). The missing v’s in big data: Viability and value. http://www.wired.com/insights/2013/05/the-missing-vs-in-big-data-viability-and-value/. Accessed February 8, 2017.

  • Boyd, D., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679.

    Google Scholar 

  • Brewster, B., Polovina, S., Rankin, G., & Andrews, S. (2014). Knowledge management and human trafficking: Using conceptual knowledge representation, text analytics and open-source data to combat organized crime. In N. Hernandez, et al. (Eds.), ICCS 2014, LNAI 8577 (pp. 104–117).

    Google Scholar 

  • Callanan, C., Gercke, M., De Marco, E., & Dries-Ziekenheiner, H. (2009). Internet blocking—balancing cybercrime responses in democratic societies. http://www.aconite.com/sites/default/files/Internet_blocking_and_Democracy.pdf. Accessed February 8, 2017.

  • Choo, C. W. (1999). The art of scanning the environment. Bulletin of the American Society for Information Science, 21–24. Accessed February/March 1999.

    Google Scholar 

  • De Marco, E. (2014). ePOOLICE, deliverables D3.3. WP3—technical and legal/ethical constraints and system framework design. https://www.epoolice.eu/EPOOLICE/servlet/document.listPublic. Accessed February 8, 2017.

  • Delhey, J., & Newton, K. (2003). Who trusts? The origins of social trust in seven societies. European Societies, 5(2), 93–137.

    Article  Google Scholar 

  • Floridi, L. (2012). Big data and their epistemological challenge. Philosophy and Technology, 25, 435–437.

    Article  Google Scholar 

  • Fried, C. (1968). Privacy: A moral analysis. Yale Law Journal, 1(77), 475–493.

    Article  Google Scholar 

  • Gerdes, A. (2014). A privacy preserving design framework in relation to an environmental scanning system for fighting organized crime. In K. Kimppa, D. Whitehouse, T. Kuusela, & J. Phahlamohlaka (Eds.), ICT and society: 11th IFIP TC9 International Conference on Human Choice and Computers, HCC11, 2014 (pp. 226–239). Turku, Finland: Springer. July 30–August 1, 2014.

    Google Scholar 

  • Hilbert, M. (2016). Big data for development: A review of promises and challenges. Development Policy Review, 34(1), 135–174.

    Article  Google Scholar 

  • IOCTA. (2015). Internet organized crime threat assessment 2015. https://www.europol.europa.eu/content/internet-organised-crime-threat-assessment-iocta-2015. Accessed February 8, 2017.

  • Johnson, D. (1994). Computer ethics. Prentice Hall.

    Google Scholar 

  • Jonas, J., & Harper, J. (2006). Effective counterterrorism and the limited role of predictive data mining (pp. 1–11). Policy analysis no. 584. December 11, 2006.

    Google Scholar 

  • Kant, I. (1785). Grundlegung zur Metaphysik der Sitten (Akademiausgabe, vol. IV). http://www.korpora.org/Kant/aa04/392.html. Accessed February 8, 2017.

  • Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1–12.

    Google Scholar 

  • Laney, D. (2001). Data management: Controlling data volume, velocity and variety. http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Accessed February 8, 2017.

  • Liotta, P. H. (2002). Boomerang effect: The convergence of national and human security. Security Dialogue © 2002 PRIO, 33(4), 473–488. (SAGE Publications).

    Google Scholar 

  • Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, Work and think. USA: John Murray.

    Google Scholar 

  • Nissenbaum, H. (2010). Privacy in context—technology, policy and the integrity of social life. Stanford: Stanford Law Books.

    Google Scholar 

  • PCAST. (2014). Report to the President—big data and privacy: A technological perspective. Executive Office of the President’s Council of Advisors on Science and Technology. https://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy_-_may_2014.pdf. Accessed February 8, 2017.

  • Peissl, W. (2003). Surveillance and security: A dodgy relationship. Journal of Contingencies and Crises Management, 1(11), 19–24.

    Article  Google Scholar 

  • Perry, W. L., McInnis, B., Price, C. C., Smith, S. C., & Hollywood, J. S. (2013). Predictive policing. The role of crime forecasting in law enforcement operation. RAND corporation, safety and justice program. RAND Corporation. http://www.rand.org/pubs/research_reports/RR233.html. Accessed April 25, 2016.

  • Rachels, J. (1975). Why privacy is important. Philosophy & Public Affairs, 4(4), 323–333.

    Google Scholar 

  • Raguse, M., Meints, M., Langfeldt, O., & Peissl, W. (2008). Prepatory action on the enhancement of the European industrial potential in the field of Security research. Technical report, PRISE. http://www.prise.oeaw.ac.at/publications.htm. Accessed April 25, 2016.

  • Ratcliffe, J. H. (2011). Intelligence-led policing. New York, USA: Routledge.

    Google Scholar 

  • Regan, P. M. (1995). Legislating privacy: Technology, social values, and public policy. USA: The University of North Carolina Press.

    Google Scholar 

  • Reiman, J. H. (1995). Driving to the panopticon: A philosophical exporation of the risks to privacy posed by the highway technology of the future. Santa Clara High Technology Law Journal, 11 (1), 27–44.

    Google Scholar 

  • Schroeck, M., Shokley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: The real-world use of big data—how innovative enterprises extract value from uncertain data. Executive Report—IBM Global Business Service, 1–20.

    Google Scholar 

  • SOCTA. (2013). Serious organized crime threat assessment 2015. https://www.europol.europa.eu/latest_publications/31. Accessed February 8, 2017.

  • Sweeney, L. (2002). K-anonymity: A model for protecting privacy. International Journal of Uncertainty Fuzziness Knowledge Based Systems, 10, 557–570.

    Article  Google Scholar 

  • Tavani, H. (1999). Informational privacy, data mining, and the internet. Ethics and Information Technology, 1, 137–145.

    Article  Google Scholar 

  • Van den Hoven, J. (1997). Privacy and the varieties of informational wrongdoing. Computers and Society, 33–37.

    Google Scholar 

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Acknowledgements

The author would like to thank Estelle De Marco, Henrik Legind Larsen, Raquel Pastor Pastor, and Javier Valls Prieto for valuable comments, which helped shape this chapter.

Research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 312651.

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Authors and Affiliations

  1. Department of Design and Communication, University of Southern Denmark, Kolding, Denmark

    Anne Gerdes

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  1. Anne Gerdes
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Correspondence to Anne Gerdes .

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Editors and Affiliations

  1. Department of Electronic Systems, Aalborg University Department of Electronic Systems, Esbjerg, Denmark

    Henrik Legind Larsen

  2. Spanish Law Enforcement Agency, Guardia Civil Spanish Law Enforcement Agency, Madrid, Spain

    José María Blanco

  3. Isdefe S.A. , Madrid, Spain

    Raquel Pastor Pastor

  4. Machine Intelligence Institute, Iona College Machine Intelligence Institute, New Rochelle, New York, USA

    Ronald R. Yager

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Gerdes, A. (2017). Big Data—Fighting Organized Crime Threats While Preserving Privacy. In: Larsen, H., Blanco, J., Pastor Pastor, R., Yager, R. (eds) Using Open Data to Detect Organized Crime Threats. Springer, Cham. https://doi.org/10.1007/978-3-319-52703-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-52703-1_5

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