Sergio Martínez

Learn More
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(More)
Centroids are key components in many data analysis algorithms such as clustering or microaggregation. They are understood as the central value that minimises the distance to all the objects in a dataset or cluster. Methods for centroid construction are mainly devoted to datasets with numerical and categorical attributes, focusing on the numerical and(More)
Home Care (HC) assistance is emerging as an effective and efficient alternative to institutionalized care, especially for the case of senior patients that present multiple co-morbidities and require life long treatments under continuous supervision. The care of such patients requires the definition of specially tailored treatments and their delivery(More)
It is not uncommon in the data anonymization literature to oppose the “old” $$k$$ k -anonymity model to the “new” differential privacy model, which offers more robust privacy guarantees. Yet, it is often disregarded that the utility of the anonymized results provided by differential privacy is quite limited, due to the amount of noise that needs to be added(More)
Microaggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in microdata releases. It has been used as an alternative to generalization and suppression to generate k-anonymous data sets, where the identity of each subject is hidden within a group of k subjects. Unlike generalization, microaggregation perturbs(More)
Structured patient data like Electronic Health Records (EHRs) are a valuable source for clinical research. However, the sensitive nature of such information requires some anonymisation procedure to be applied before releasing the data to third parties. Several studies have shown that the removal of identifying attributes, like the Social Security Number, is(More)
Using microdata provided by statistical agencies has many benefits from the data mining point of view. However, such data often involve sensitive information that can be directly or indirectly related to individuals. An appropriate anonymisation process is needed to minimise the risk of disclosure. Several masking methods have been developed to deal with(More)
A common view in some data anonymization literature is to oppose the "old'' k-anonymity model to the "new'' differential privacy model, which offers more robust privacy guarantees. However, the utility of the masked results provided by differential privacy is usually limited, due to the amount of noise that needs to be added to the output, or because(More)
The exploitation of sensible data associated to individuals requires a proper anonymization in order to preserve the privacy. Even though several masking methods have been designed for numerical data, very few of them deal with textual information. During the masking process, information loss should be minimized in order to enable a proper analysis of data(More)