Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Inference Control in Statistical Databases, From Theory to Practice
Maximum entropy simulation for microdata protection
Statistics and Computing
Minimum Spanning Tree Partitioning Algorithm for Microaggregation
IEEE Transactions on Knowledge and Data Engineering
A class of heuristics for the constrained forest problem
Discrete Applied Mathematics
Efficient multivariate data-oriented microaggregation
The VLDB Journal — The International Journal on Very Large Data Bases
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
A new framework to automate constrained microaggregation
Proceedings of the ACM first international workshop on Privacy and anonymity for very large databases
Communication: A class of heuristics for the constrained forest problem
Discrete Applied Mathematics
Hybrid microdata using microaggregation
Information Sciences: an International Journal
Privacy preservation by independent component analysis and variance control
Proceedings of the 20th ACM international conference on Information and knowledge management
Optimal multivariate 2-microaggregation for microdata protection: a 2-approximation
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Using mahalanobis distance-based record linkage for disclosure risk assessment
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Information fusion in data privacy: A survey
Information Fusion
Another greedy heuristic for the constrained forest problem
Operations Research Letters
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In previous work by Domingo-Ferrer et al., rank swapping and multivariate microaggregation has been identified as well-performing masking methods for microdata protection. Recently, Dandekar et al. proposed using synthetic microdata, as an option, in place of original data by using Latin hypercube sampling (LHS) technique. The LHS method focuses on mimicking univariate as well as multivariate statistical characteristics of original data. The LHS-based synthetic data does not allow one to one comparison with original data. This prevents estimating the overall information loss by using current measures. In this paper we utilize unique features of LHS method to create hybrid data sets and evaluate their performance relative to rank swapping and multivariate microaggregation using generalized information loss and disclosure risk measures.