Abstract
In this paper, we attempt to relate two different approaches to data privacy – the technological approach, embodied in the current Privacy-preserving Data Mining work, and the economic regulations approach. Our main thesis is that none of these two approaches alone will be able to address the important, growing data privacy issues. We advocate a hybrid system, combining both approaches in a complementary manner. We present a view of privacy in the context of an accepted taxonomy of economic goods, stating the question: if privacy is exchanged and traded, then what kind of good is it? We also show that, viewed in the light of an established economic growth theory, involving privacy in the growth process leads to a steady state growth.
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References
Acemoglu, D.: Introduction to Modern Economic Growth. Princeton University Press, Princeton (2009)
Acquisti, A.: Privacy in Electronic Commerce and the Economics of Immediate Gratification. In: Proceedings of ACM Electronic Commerce Conference (EC 2004), pp. 21–29. ACM Press, New York (2004)
Acquisti, A., Grossklags, J.: IEEE Security & Privacy, 24–30 (January/February 2005)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. ACM SIGMOD Record, 439–450 (2000)
Arrow, K.J.: Uncertainty and the welfare economics of medical care. American Economic Review 53, s.941–s.973 (1963)
Becker, G.S.: Human capital: a theoretical and empirical analysis. Princeton University Press, Princeton (1964)
Becker, G.S., Murphy, K.M.: Human capital, fertility, and economic growth. Journal of Political Economy (1990)
Berendt, B., Teltzrow, M.: Addressing Users’ Privacy Concerns for Improving Personalization Quality: Towards an Integration of User Studies and Algorithm Evaluation. In: Mobasher, B., Anand, S.S. (eds.) ITWP 2003. LNCS (LNAI), vol. 3169, pp. 69–88. Springer, Heidelberg (2005)
Bergadano, F., Matwin, S., Michalski, R., Zhang, J.: Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System. Machine Learning 8(1), 5–43
Clifton, C.W.: What is Privacy? Critical Steps for Privacy-Preserving Data Mining. In: Workshop on Privacy and Security Aspects of Data Mining (2005)
Domar, E.: Capital expansion, rate of growth and employment. Econometrica (April 1946)
Domingo-Ferrer, J. (ed.): Inference Control in Statistical Databases. LNCS, vol. 2316. Springer, Heidelberg (2002)
Du, W., Zhan, Z.: Using randomized response techniques for privacy-preserving data mining. In: Proceedings of The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 510 (2003)
Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy preserving mining of association rules. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 217–228 (2002)
Friedman, A., Schuster, A., Wolff, R.: k-Anonymous Decision Tree Induction. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 151–162. Springer, Heidelberg (2006)
Harrod, R.F.: An essay in dynamic theory. The Economic Journal 49, 193 (1939a)
Harrod, R.F.: Second essay in dynamic theory. The Economic Journal 70, 278 (1939b)
Iyengar, V.S.: Transforming data to satisfy privacy constraints. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 279–288 (2002)
Jiang, W., Atzori, M.: Secure Distributed k-Anonymous Pattern Mining. In: Proceedings of the Sixth International Conference on Data Mining. IEEE Computer Society, Los Alamitos (2006)
Kargupta, H., Das, K., Liu, K.: Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 523–531. Springer, Heidelberg (2007)
Kantarcioglu, M., Clifton, C.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Transactions on Knowledge and Data Engineering 16, 1026–1037 (2004)
Kornai, J.: Economics of Shortage. North-Holland, Amsterdam (1980)
Laudon, K.: Markets and Privacy. Communications of the ACM 39(9) (September 1996)
Lucas, R.E.: Expectations and the neutrality of Money. Journal of Economic Theory 4, 103–124 (1972)
Lucas, R.E.: On the mechanics of economic development. Journal of Monetary Economics 22(1) (1998)
Malin, B.A.: An Evaluation of the Current State of Genomic Data Privacy Protection Technology and a Roadmap for the Future. Journal of the American Medical Informatics Association 12, 28 (2005)
Maslow, A.H.: A Theory of Human Motivation. Psychological Review 50, 370–396 (1943)
Moor, J.: Towards a Theory of Privacy in the Information Age. Computers and Society 27, 27–32 (1997)
Oliveira, S.R.M., Zaïane, O.R., Saygin, Y.: Secure Association Rule Sharing. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 74–85. Springer, Heidelberg (2004)
Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, p. 223. Springer, Heidelberg (1999)
Putnam, R.: Social Capital - Measurement and Consequences. In: Helliwell, J.F. (ed.) The Contribution of Human and Social Capital to Sustained Economic Growth and Well-Being; International Symposium Report, Quebec (2001)
Ramsey, F.: The mathematical theory of saving. Economic Journal 38 (1928)
Rasi, G.: Privacy as Quality in Modern Economy. In: The 26th International Conference on Privacy and Personal Data Protection, Wroclaw, Poland (2004)
Routledge, B.R., von Amsberg, J.: Social Capital and Growth. Journal of Monetary Economics 50(1), 167–193 (2002)
Solow, R.: Growth theory and after. The American Economic Review 78(3) (June 1988)
Solow, R.: A contribution to the theory of economic growth. Quarterly Journal of Economics 70 (1956)
Spiekermann, S., Grossklags, J., Berendt, B.: E-privacy in 2nd generation E-Commerce: privacy preferences versus actual behavior. In: Proceedings of the ACM Conference on Electronic Commerce (EC 2001), Tampa, FL, October 14-17 (2001)
Subramaniam, H., Wright, R.N., Yang, Z.: Experimental analysis of privacy-preserving statistics computation. In: Proc. of the VLDB Worshop on Secure Data Management, pp. 55–66 (2004)
Sweeney, L.: Computational Disclosure Control: A Primer on Data Privacy Protection, Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science (2001)
Vaidya, J., Clifton, C.: Privacy Preserving Association Rule Mining in Vertically Partitioned Data. In: Conference on Knowledge Discovery in Data, pp. 639–644 (2002)
Varian, H.: Economic Aspects of Personal Privacy. U.S. Dept. of Commerce, Privacy and Self-Regulation in the Information Age (1996)
Varian, H.: System Reliability and Free Riding. In: Camp, L.J., Lewis, S. (eds.) Economics of Information Security. Advances in Information Security, vol. 12, pp. 1–15. Kluwer Academic Publishers, Dordrecht (2004)
Westin, A.: Privacy and Freedom. Atheneum, NY (1967)
Yao, A.: How to Generate and Exchange Secrets. In: 27th FOCS (1986)
Zhan, Z., Matwin, S.: Privacy-prteserving Data Mining in Electronic Surveys. In: ICEB 2004, pp. 1179–1185 (2004)
Zhan, J., Matwin, S., Chang, L.: Privacy-Preserving Collaborative Association Rule Mining. In: DBSec, pp. 153–165 (2005)
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Matwin, S., Szapiro, T. (2010). Data Privacy: From Technology to Economics. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning II. Studies in Computational Intelligence, vol 263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05179-1_3
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DOI: https://doi.org/10.1007/978-3-642-05179-1_3
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