Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Exact and approximate methods for data directed microaggregation in one or more dimensions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A Polynomial Algorithm for Optimal Univariate Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Efficient multivariate data-oriented microaggregation
The VLDB Journal — The International Journal on Very Large Data Bases
Attribute selection in multivariate microaggregation
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Ordered Data Set Vectorization for Linear Regression on Data Privacy
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
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Microaggregation is one of the most commonly employed microdata protection methods. This method builds clusters of at least koriginal records and replaces the records in each cluster with the centroid of the cluster. Usually, when records are complex, i.e., the number of attributes of the data set is large, this data set is split into smaller blocks of attributes and microaggregation is applied to each block, successively and independently. In this way, the information loss when collapsing several values to the centroid of their group is reduced, at the cost of losing the k-anonymity property when at least two attributes of different blocks are known by the intruder.In this work, we present a new microaggregation method called One dimension microaggregation(Mic1D茂戮驴 茂戮驴). This method gathers all the values of the data set into a single sorted vector, independently of the attribute they belong to. Then, it microaggregates all the mixed values together. Our experiments show that, using real data, our proposal obtains lower disclosure risk than previous approaches whereas the information loss is preserved.