Miguel Núñez del Prado Cortez

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Due to the emergence of geolocated applications, more and more mobility traces are generated on a daily basis and collected in the form of geolocated datasets. If an unauthorized entity can access this data, it can used it to infer personal information about the individuals whose movements are contained within these datasets, such as learning their home and(More)
—With the advent of GPS-equipped devices, a massive amount of location data is being collected, raising the issue of the privacy risks incurred by the individuals whose movements are recorded. In this work, we focus on a specific inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular individual(More)
—A geolocalised system generally belongs to an individual and as such knowing its location reveals the location of its owner, which is a direct threat against his privacy. To protect the privacy of users, a sanitization process, which adds uncertainty to the data and removes some sensible information, can be performed but at the cost of a decrease of(More)
—GEPETO (for GEoPrivacy-Enhancing TOolkit) is a flexible software that can be used to visualize, sanitize, perform inference attacks and measure the utility of a particular geolocated dataset. The main objective of GEPETO is to enable a data curator (e.g., a company, a governmental agency or a data protection authority) to design, tune, experiment and(More)
Clustering is an unsupervised learning technique used to group a set of elements into non-overlapping clusters based on some predefined dissimilarity function. In our context, we rely on clustering algorithms to extract points of interest in human mobility as an inference attack for quantifying the impact of the privacy breach. Thus, we focus on the input(More)
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