A data distortion by probability distribution
ACM Transactions on Database Systems (TODS)
ACM Transactions on Database Systems (TODS)
A study on the protection of statistical data bases
SIGMOD '77 Proceedings of the 1977 ACM SIGMOD international conference on Management of data
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Auditing Interval-Based Inference
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
Parity-based inference control for multi-dimensional range sum queries
Journal of Computer Security
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICICS'07 Proceedings of the 9th international conference on Information and communications security
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
On optimizing the k-ward micro-aggregation technique for secure statistical databases
ACISP'06 Proceedings of the 11th Australasian conference on Information Security and Privacy
A fixed structure learning automaton micro-aggregation technique for secure statistical databases
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Multivariate microaggregation by iterative optimization
Applied Intelligence
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Microaggregation is a statistical disclosure control technique for protecting microdata (i.e., individual records), which are important products of statistical offices. The basic idea of microaggregation is to cluster individual records in microdata into a number of mutually exclusive groups prior to publication, and then publish the average over each group instead of individual records. Previous methods require fixed or variable group size in clustering in order to reduce information loss. However, the security aspect of microaggregation has not been extensively studied. We argue that the group size requirement is not enough for protecting the privacy of microdata. We propose a new microaggregation method, which we call secure-k-Ward, to enhance the individual's privacy. Our method, which is optimization based, minimizes information loss and overall mean deviation while at the same time guarantees that the security requirement for protecting the microdata is satisfied.