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In this paper we propose a new method to anonymize (share relevant and detailed information while not naming names) and protect data sets (minimize the utility loss) based on Factor Analysis. The method basically consists of obtaining the factors, which are uncorrelated, protecting them and undoing the transformation in order to get interpretable protected variables. We first show how to proceed when...
The data anonymization landscape has become quite complex in the last decades. On the methodology side, the statistical disclosure control methods designed in official statistics have been supplemented by a number of privacy models proposed by computer scientists. On the data side, static data sets now coexist with big data, and particularly data streams. In the quest for a unified and conceptually...
t-Closeness was introduced as an improvement of the well-known k-anonymity privacy model for data release. On the other hand, e-differential privacy was originally proposed as a privacy property for answers to on-line database queries and it has been very welcome in academic circles. In spite of their quite diverse origins and motivations, we show in this paper that t-closeness and e-differential...
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