Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
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
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
The Security of Confidential Numerical Data in Databases
Information Systems Research
Exact and approximate methods for data directed microaggregation in one or more dimensions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A Polynomial Algorithm for Optimal Univariate Microaggregation
IEEE Transactions on Knowledge and Data Engineering
On Privacy-Preserving Access to Distributed Heterogeneous Healthcare Information
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 6 - Volume 6
Perturbing Nonnormal Confidential Attributes: The Copula Approach
Management Science
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
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Auditing and Inference Control in Statistical Databases
IEEE Transactions on Software Engineering
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
A TTP-free protocol for location privacy in location-based services
Computer Communications
On the disclosure risk of multivariate microaggregation
Data & Knowledge Engineering
A Linear-Time Multivariate Micro-aggregation for Privacy Protection in Uniform Very Large Data Sets
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
Importance partitioning in micro-aggregation
Computational Statistics & Data Analysis
A new framework to automate constrained microaggregation
Proceedings of the ACM first international workshop on Privacy and anonymity for very large databases
Density-based microaggregation for statistical disclosure control
Expert Systems with Applications: An International Journal
Micro-SOM: A Linear-Time Multivariate Microaggregation Algorithm Based on Self-Organizing Maps
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Hybrid microdata using microaggregation
Information Sciences: an International Journal
Coprivacy: towards a theory of sustainable privacy
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
Edit constraints on microaggregation and additive noise
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Microaggregation- and permutation-based anonymization of movement data
Information Sciences: an International Journal
Privacy protection in location-based services through a public-key privacy homomorphism
EuroPKI'07 Proceedings of the 4th European conference on Public Key Infrastructure: theory and practice
A modification of the Lloyd algorithm for k-anonymous quantization
Information Sciences: an International Journal
Anonymization methods for taxonomic microdata
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
Optimal univariate microaggregation with data suppression
Journal of Systems and Software
Class-Restricted Clustering and Microperturbation for Data Privacy
Management Science
MAGE: A semantics retaining K-anonymization method for mixed data
Knowledge-Based Systems
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Microaggregation is a family of methods for statistical disclosure control (SDC) of microdata (records on individuals and/or companies), that is, for masking microdata so that they can be released without disclosing private information on the underlying individuals. Microaggregation techniques are currently being used by many statistical agencies. The principle of microaggregation is to group original database records into small aggregates prior to publication. Each aggregate should contain at least k records to prevent disclosure of individual information, where k is a constant value preset by the data protector. In addition to it being a good masking method, microaggregation has recently been shown useful to achieve k-anonymity. In k-anonymity, the parameter k specifies the maximum acceptable disclosure risk, so that, once a value for k has been selected, the only job left is to maximize data utility: if microaggregation is used to implement k-anonymity, maximizing utility can be achieved by microaggregating optimally, i.e. with minimum within-groups variability loss. Unfortunately, optimal microaggregation can only be computed in polynomial time for univariate data. For multivariate data, it has been shown to be NP-hard. We present in this paper a polynomial-time approximation to microaggregate multivariate numerical data for which bounds to optimal microaggregation can be derived at least for two different optimality criteria: minimum within-groups Euclidean distance and minimum within-groups sum of squares. Beyond the theoretical interest of being the first microaggregation proposal with proven approximation bounds for any k, our method is empirically shown to be comparable to the best available heuristics for multivariate microaggregation.