In the context of Statistical Disclosure Control, microaggregation is a privacy preserving method aimed to mask sensitive microdata prior to publication. It iteratively creates clusters of, at least, k elements, and replaces them by their prototype so that they become k-indistinguishable (anonymous). This data transformation produces a loss of information with regards to the original dataset wh...

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...

Microdata protection in statistical databases has recently become a major societal concern and has been intensively studied in recent years. Statistical Disclosure Control (SDC) is often applied to statistical databases before they are released for public use. Microaggregation for SDC is a family of methods to protect microdata from individual identification. SDC seeks to protect microdata in s...

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...

In this paper, we propose an effective microaggregation algorithm to produce a more useful protected data for publishing. Microaggregation is mapped to a clustering problem with known minimum and maximum group size constraints. In this scheme, the goal is to cluster n records into groups of at least k and at most 2k_1 records, such that the sum of the within-group squ...

This paper presents a K-means clustering technique that satisfies the biobjective function to minimize the information loss and maintain k-anonymity. The proposed technique starts with one cluster and subsequently partitions the dataset into two or more clusters such that the total information loss across all clusters is the least, while satisfying the k-anonymity requirement. The structure of ...

In this paper, we propose an effective microaggregation algorithm to produce a more useful protected data for publishing. Microaggregation is mapped to a clustering problem with known minimum and maximum group size constraints. In this scheme, the goal is to cluster n records into groups of at least k and at most 2k−1 records, such that the sum of the within-group squared error (SSE) is minimiz...

In previous work by Domingo-Ferrer et al., rank swapping and multivariate microaggregation has been identified as well-performing masking methods for microdata protection. Recently, Dandekar et al. proposed using synthetic microdata, as an option, in place of original data by using Latin hypercube sampling (LHS) technique. The LHS method focuses on mimicking univariate as well as multivariate s...

Nowadays, the management of sequential and temporal data is an increasing need in many data mining processes. Therefore, the development of new privacy preserving data mining techniques for sequential data is a crucial need to ensure that sequence data analysis is performed without disclosure sensitive information. Although data analysis and protection are very different processes, they share a...