Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Computational Statistics & Data Analysis
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
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
Efficient multivariate data-oriented microaggregation
The VLDB Journal — The International Journal on Very Large Data Bases
Secure computation with horizontally partitioned data using adaptive regression splines
Computational Statistics & Data Analysis
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
Bregman divergences in the (m×k)-partitioning problem
Computational Statistics & Data Analysis
Optimal univariate microaggregation with data suppression
Journal of Systems and Software
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One of the techniques of data holders for the protection of confidentiality of continuous data is that of micro-aggregation. Rather than releasing raw data (individual records), micro-aggregation releases the averages of small groups and thus reduces the risk of identity disclosure. At the same time the method implies loss of information and often distorts the data. Thus, the choice of groups is very crucial to minimize the information loss and the data distortion. No exact polynomial algorithms exist up to date for optimal micro-aggregation, and so the usage of heuristic methods is necessary. A heuristic algorithm, based on the notion of importance partitioning, is proposed and it is shown that compared with other micro-aggregation heuristics achieves improved performance.