Abstract
The protection of personal privacy is paramount, and consequently many efforts have been devoted to the study of data protection techniques. Governments, statistical agencies and corporations must protect the privacy of the individuals while guaranteeing the right of the society to knowledge. Microaggregation is one of the most promising solutions to deal with this praiseworthy task. However, its high computational cost prevents its use with large amounts of data. In this article we propose a new microaggregation algorithm that uses self-organizing maps to scale down the computational costs while maintaining a reasonable loss of information.
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References
Brand, R.: Microdata protection through noise addition. In: Inference Control in Statistical Databases, From Theory to Practice, London, UK, pp. 97–116. Springer, Heidelberg (2002)
Moore Jr., R.: Controlled data-swapping techniques for masking public use microdata sets. Technical report, Statistical Research Division Report Series, RR 96-04, US Bureau of the Census, Washington D.C. (1996)
Burridge, J.: Information preserving statistical obfuscation. Statistics and Computing 13(4), 321–327 (2003)
Domingo-Ferrer, J., Sebé, F., Solanas, A.: A polynomial-time approximation to optimal multivariate microaggregation. Comput. Math. Appl. 55(4), 714–732 (2008)
Adam, N.R., Worthmann, J.C.: Security-control methods for statistical databases: a comparative study. ACM Comput. Surv. 21(4), 515–556 (1989)
Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13(6), 1010–1027 (2001)
Sweeney, L.: k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 10(5), 557–570 (2002)
Oganian, A., Domingo-Ferrer, J.: On the complexity of optimal microaggregation for statistical disclosure control. Statistical Journal of the United Nations Economic Comission for Europe 18(4), 345–354 (2001)
Laszlo, M., Mukherjee, S.: Minimum spanning tree partitioning algorithm for microaggregation. IEEE Transactions on Knowledge and Data Engineering 17(7), 902–911 (2005)
Domingo-Ferrer, J., Torra, V.: Ordinal, continuous and heterogenerous k-anonymity through microaggregation. Data Mining and Knowledge Discovery 11(2), 195–212 (2005)
Solanas, A., Martínez-Ballesté, A.: V-MDAV: Variable group size multivariate microaggregation. In: COMPSTAT 2006, Rome, pp. 917–925 (2006)
Solanas, A., Martinez-Balleste, A., Mateo-Sanz, J.M., Domingo-Ferrer, J.: Multivariate microaggregation based on genetic algorithms. In: 3rd International IEEE Conference on Intelligent Systems, pp. 65–70 (2006)
Martinez-Balleste, A., Solanas, A., Domingo-Ferrer, J., Mateo-Sanz, J.: A genetic approach to multivariate microaggregation for database privacy. In: IEEE 23rd International Conference on Data Engineering Workshop, April 17-20, pp. 180–185 (2007)
Solanas, A., Pietro, R.: A linear-time multivariate micro-aggregation for privacy protection in uniform very large data sets. In: Torra, V., Narukawa, Y. (eds.) MDAI 2008. LNCS (LNAI), vol. 5285, pp. 203–214. Springer, Heidelberg (2008)
Kohonen, T.: The self-organizing map. Proceedings of the IEEE 78(9), 1464–1480 (1990)
Kaski, S., Lagus, K.: Comparing self-organizing maps. In: Vorbrüggen, J.C., von Seelen, W., Sendhoff, B. (eds.) ICANN 1996. LNCS, vol. 1112, pp. 809–814. Springer, Heidelberg (1996)
Davies, D., Bouldin, D.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 2(1), 224–227 (1979)
Kangas, J.: Increasing the error tolerance in transmission of vector quantized images by self-organizing map. In: FogelmanSoulie, F., Gallinari, P. (eds.) Proc. ICANN 1995, Int. Conf. on Artificial Neural Networks, pp. 287–291 (1995)
Vesanto, J.: SOM-Based data visualization methods. In: Intelligent Data Analysis, vol. 3, pp. 111–126 (1999)
Koikkalainen, P., Oja, E.: Self-organizing hierarchical feature maps. In: International Joint Conference on Neural Networks (IJCNN 1990), pp. 279–284 (1990)
Kangas, J., Kohonen, T., Laaksonen, J.: Variants of the Self-organizing map. IEEE Transactions on Neural Networks 1(1), 93–99 (1990)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-organizing map in Matlab: the SOM toolbox. In: Matlab DSP Conference, pp. 35–40 (1999)
Roussinov, D.G., Chen, H.: A scalable self-organizing map algorithm for textual classification: A neural network approach to thesaurus generation. Communication Cognition and Artificial Intelligence 15, 81–112 (Spring 1998)
Vesanto, J.: Neural network tool for data mining: Som toolbox. In: Proceedings of Symposium on Tool Environments and Development Methods for Intelligent Systems (TOOLMET 2000), Oulu, Finland, Oulun yliopistopiano, pp. 184–196 (2000)
Brand, R., Domingo-Ferrer, J., Mateo-Sanz, J.: Reference data sets to test and compare SDC methods for protection of numerical microdata. European Project IST-2000-25069 CASC (2002)
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Solanas, A., Gavalda, A., Rallo, R. (2009). Micro-SOM: A Linear-Time Multivariate Microaggregation Algorithm Based on Self-Organizing Maps. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_55
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DOI: https://doi.org/10.1007/978-3-642-04274-4_55
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