Microaggregation is an efficient Statistical Disclosure Control (SDC) perturbative technique for microdata protection. It is a unified approach and naturally satisfies k-Anonymity without generalization or suppression of data. Various microaggregation techniques: fixed-size and data-oriented for univariate and multivariate data exists in the literature. These methods have been evaluated using t...

In recent years, there has been an alarming increase of online identity theft and attacks using personally identifiable information. The goal of privacy preservation is to de-associate individuals from sensitive or microdata information. Microaggregation techniques seeks to protect microdata in such a way that can be published and mined without providing any private information that can be link...

BACKGROUND Engineered nanoparticles (NPs) can induce platelet activation and aggregation, but the mechanisms underlying these interactions are not well understood. This could be due in part to use of devices that study platelet function under quasi-static conditions with low sensitivity to measure platelet microaggregation. Therefore, in this study we investigated the pharmacological pathways a...

Microaggregation is one of the most commonly employed microdata protection methods. The basic idea of microaggregation is to anonymize data by aggregating original records into small groups of at least k elements and, therefore, preserving k-anonymity. Usually, in order to avoid information loss, when records are large, i.e., the number of attributes of the data set is large, this data set is s...

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 o...

Data aggregation is a central principle underlying many applications in computer science, from artificial intelligence to data security and privacy. Microaggregation is a special clustering problem where the goal is to cluster a set of points into groups of at least k points in such a way that groups are as homogeneous as possible. A usual homogeneity criterion is the minimization of the within...

Microaggregation is a statistical disclosure control technique for microdata. Raw microdata (i. e. individual records) are grouped into small aggregates prior to publication. Each aggregate should contain at least k records to prevent disclosure of individual information. Fixedsize microaggregation consists of taking fixed-size microaggregates (size k). Data-oriented microaggregation (with vari...

In recent years, there has been an alarming increase of online identity theft and attacks using personally identifiable information. The goal of privacy preservation is to de-associate individuals from sensitive or microdata information. Microaggregation techniques seeks to protect microdata in such a way that can be published and mined without providing any private information that can be link...

Microaggregation is a cardinality-constrained clustering problem that arose in the context of data privacy. In microaggregation, the number of clusters is not fixed beforehand, but each cluster must have at least k elements. We illustrate in this paper that microaggregation can be applied for decision making in areas other than privacy. Specifically, we focus on the service facility location pr...

Statistical disclosure control (SDC) methods reconcile the need to release information to researchers with the need to protect privacy of individual records. Microaggregation is a SDC method that protects data subjects by guarantying k-anonymity: Records are partitioned into groups of size at least k and actual data values are replaced by the group means so that each record in the group is indi...