Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
LHS-Based Hybrid Microdata vs Rank Swapping and Microaggregation for Numeric Microdata Protection
Inference Control in Statistical Databases, From Theory to Practice
Disclosure Risk Assessment in Perturbative Microdata Protection
Inference Control in Statistical Databases, From Theory to Practice
Exact and approximate methods for data directed microaggregation in one or more dimensions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A Polynomial Algorithm for Optimal Univariate Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Minimum Spanning Tree Partitioning Algorithm for Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata
Data Mining and Knowledge Discovery
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Rethinking rank swapping to decrease disclosure risk
Data & Knowledge Engineering
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
Constrained Microaggregation: Adding Constraints for Data Editing
Transactions on Data Privacy
Towards knowledge intensive data privacy
DPM'10/SETOP'10 Proceedings of the 5th international Workshop on data privacy management, and 3rd international conference on Autonomous spontaneous security
Information fusion in data privacy: A survey
Information Fusion
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Data protection methods from privacy preserving data mining and statistical disclosure control can introduce perturbation in the data. While this perturbation helps to protect the privacy of the respondents, it can introduce inconsistencies and errors. Moreover, the data are normally edited after collection to ensure its correctness and fix inconsistencies, and perturbation methods can introduce new errors in such data. In this paper we present a framework to automate the protection of data with a perturbative method, microaggregation, while at the same time ensuring that no inconsistencies and errors are introduced. That is, the data are microaggregated preserving a set of given constraints (edit constraints).