Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Microdata Protection through Noise Addition
Inference Control in Statistical Databases, From Theory to Practice
A Polynomial Algorithm for Optimal Univariate 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
Rethinking rank swapping to decrease disclosure risk
Data & Knowledge Engineering
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Constrained Microaggregation: Adding Constraints for Data Editing
Transactions on Data Privacy
Privacy Preserving Data Mining
Privacy Preserving Data Mining
Information fusion in data privacy: A survey
Information Fusion
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Privacy preserving data mining and statistical disclosure control propose several perturbative methods to protect the privacy of the respondents. Such perturbation can introduce inconsistencies to the sensitive data. Due to this, data editing techniques are used in order to ensure the correctness of the collected data before and after the anonymization. In this paper we propose a methodology to protect microdata based on noise addition that takes data edits into account. Informally, when adding noise causes a constraint to fail, we apply a process of noise swapping to preserve the edit constraint. We check its suitability against the constrained microaggregation, a method for microaggregation that avoids the introduction of such inconsistencies.