Abstract
PRAM (Post Randomization Method) was introduced in 1997 but it is still one of the least used methods in statistical categorical data protection. This fact is because of the difficulty to obtain a good transition matrix in order to obtain a good protection. In this paper, we describe how to obtain a better protection using an evolutionary algorithm with integrated information loss and disclosure risk measures to find the best matrix. We also provide experiments using a real dataset of 1000 records in order to empirically evaluate the application of this technique.
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Caruana, R.A., Schaffer, J.D.: Representation and hidden bias: Gray vs binary coding for genetic algorithms. In: Proc. of the 5th Int. Conf. on Machine Learning, pp. 153–161. Morgan Kaufmann, Los Altos (1988)
De Wolf, P.P., Van Gelder, I.: An empirical evaluation of PRAM. Discussion paper 04012. Statistics Netherlands, Voorburg/Heerlen (2004)
Fienberg, S.E.: Conflict between the needs for access to statistical information and demands for confidentiality. Journal of Official Statistics 10(2), 115–132 (1994)
Gouweleeuw, J., Kooiman, P., Willenborg, L., de Wolf, P.P.: Post randomization for statistical disclosure control: Theory and implementation. Journal of Official Statistics 14(4), 463–478 (1998)
De Wolf, P.P., Gouweleeuw, J., Kooiman, P., Willenborg, L.: Reflections on pram. In: Statistical Data Protection, pp. 337–349. Office for Official Publications of the European Communities, Luxembourg (1998)
Kooiman, P., Willenborg, L., Gouweleeuw, J.: A method for disclosure limitation of microdata. Research paper 9705, Statistics Netherlands, Voorburg (1997)
Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13(6), 1010–1027 (2001)
Domingo-Ferrer, J., Torra, V.: A quantitative comparison of disclosure control methods for microdata. In: Doyle, P., Lane, J.I., Theeuwes, J.J.M., Zayatz, L.V. (eds.) Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, ch. 6, pp. 111–133. Elsevier, Amsterdam (2001)
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Marés, J., Torra, V. (2010). PRAM Optimization Using an Evolutionary Algorithm. In: Domingo-Ferrer, J., Magkos, E. (eds) Privacy in Statistical Databases. PSD 2010. Lecture Notes in Computer Science, vol 6344. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15838-4_9
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DOI: https://doi.org/10.1007/978-3-642-15838-4_9
Publisher Name: Springer, Berlin, Heidelberg
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