Microaggregation is an anonymization technique consisting on partitioning the data into clusters no smaller than k elements and then replacing the whole cluster by its prototypical representant. Most of microaggregation techniques work on numerical attributes. However, many data sets are described by heterogeneous types of data, i.e., numerical and categorical attributes. In this paper we propo...

Privacy-preserving data mining (PPDM) is a subdiscipline of computer science which in many respects is parallel to statistical disclosure control (SDC) within statistics. See [12] for a survey of recent developments in PPDM. We focus here on the connections between k-anonymity, a concept arisen in the PPDM community, and microaggregation, a family of methods developed within SDC. This is discus...

Microaggregation is one of the most frequently applied statistical disclosure control techniques for continuous data. The basic principle of microaggregation is to group the observations in a data set and to replace them by their corresponding group means. However, while reducing the disclosure risk of data files, the technique also affects the results of statistical analyses. The paper deals w...

A problem statistical offices are increasingly faced with is guaranteeing confidentiality when releasing microdata sets. One method to provide safe microdata to is to reduce the information content of a data set by means of masking procedures. A widely discussed masking procedure is microaggregation, a technique where observations are grouped and replaced with their corresponding group means. H...

Microaggregation is one of the most frequently applied statistical disclosure control techniques for continuous data. The basic principle of microaggregation is to group the observations in a data set and to replace them by their corresponding group means. However, while reducing the disclosure risk of data files, the technique also affects the results of statistical analyses. The paper deals w...

Microaggregation is a classical statistical disclosure control technique which requires the input data to be partitioned into clusters while adhering specified size constraints. We provide novel exact algorithms and lower bounds for task of microaggregating given network considering both unrestricted connected clusterings, analyze these from perspective parameterized complexity paradigm. Altoge...

Although Artificial Intelligent (AI) techniques have been used in various applications, their use in maintaining security in Statistical DataBases (SDBs) has not been reported. This paper presents results, that to the best of our knowledge is pioneering, by which concepts from causal networks are used to secure SDBs. We consider the MicroAggregation Problem (MAP) in secure SDBs which involves p...

With the phenomenal change in a way data are collected, stored and disseminated among various data analyst there is an urgent need of protecting the privacy of data. As when individual data get disseminated among various users, there is a high risk of revelation of sensitive data related to any individual, which may violate various legal and ethical issues. Statistical Disclosure Control (SDC) ...

Inhibitors: Causation or Association? To the Editor: Trials of glycoprotein IIb/IIIa inhibitors (GPI) in acute coronary syndromes (ACS) reveal that thrombocytopenia occurs with an incidence of up to 3.7%.1 McClure et al2 confirm the association of thrombocytopenia and ACS and correlate this with adverse bleeding and ischemic events. However, unlike previous studies, they found no association be...