Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Disclosure Risk Assessment in Perturbative Microdata Protection
Inference Control in Statistical Databases, From Theory to Practice
A Privacy-Enhanced Microaggregation Method
FoIKS '02 Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems
k-anonymity: a model for protecting privacy
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
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient multivariate data-oriented microaggregation
The VLDB Journal — The International Journal on Very Large Data Bases
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
TFRP: An efficient microaggregation algorithm for statistical disclosure control
Journal of Systems and Software
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Micro-aggregation-based heuristics for p-sensitive k-anonymity: one step beyond
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
An Anonymity Model Achievable Via Microaggregation
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
An Improved V-MDAV Algorithm for l-Diversity
ISIP '08 Proceedings of the 2008 International Symposiums on Information Processing
On the disclosure risk of multivariate microaggregation
Data & Knowledge Engineering
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Success in Evolutionary Computation
Success in Evolutionary Computation
Density-based microaggregation for statistical disclosure control
Expert Systems with Applications: An International Journal
Hybrid microdata using microaggregation
Information Sciences: an International Journal
A family of enhanced (L,α)-diversity models for privacy preserving data publishing
Future Generation Computer Systems
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
Minimizing minimality and maximizing utility: analyzing method-based attacks on anonymized data
Proceedings of the VLDB Endowment
Extended k-anonymity models against sensitive attribute disclosure
Computer Communications
An approximate microaggregation approach for microdata protection
Expert Systems with Applications: An International Journal
Efficient fuzzy ranking queries in uncertain databases
Applied Intelligence
New Record Ordering Heuristics for Multivariate Microaggregation.
New Record Ordering Heuristics for Multivariate Microaggregation.
Successive Group Selection for Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Using TF-IDF to hide sensitive itemsets
Applied Intelligence
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Microaggregation is a well-known perturbative approach to publish personal or financial records while preserving the privacy of data subjects. Microaggregation is also a mechanism to realize the k-anonymity model for Privacy Preserving Data Publishing (PPDP). Microaggregation consists of two successive phases: partitioning the underlying records into small clusters with at least聽k records and aggregating the clustered records by a special kind of cluster statistic as a replacement. Optimal multivariate microaggregation has been shown to be NP-hard. Several heuristic approaches have been proposed in the literature. This paper presents an iterative optimization method based on the optimal solution of the microaggregation problem (IMHM). The method builds the groups based on constrained clustering and linear programming relaxation and fine-tunes the results within an integrated iterative approach. Experimental results on both synthetic and real-world data sets show that IMHM introduces less information loss for a given privacy parameter, and can be adopted for different real world applications.