Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Microdata Protection through Noise Addition
Inference Control in Statistical Databases, From Theory to Practice
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Information preserving statistical obfuscation
Statistics and Computing
Minimum Spanning Tree Partitioning Algorithm for Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
A Linear-Time Multivariate Micro-aggregation for Privacy Protection in Uniform Very Large Data Sets
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
A Genetic Approach to Multivariate Microaggregation for Database Privacy
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
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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.