ESQUIVEL-QUIROS, Luis Gustavo; BARRANTES, Elena Gabriela    DARLINGTON, Fernando Esponda. Privacy measurement framework. []. , 31, pp.66-81. ISSN 1646-9895.  https://doi.org/10.17013/risti.31.66-81.

The grown penalties for privacy violations motivate the definition of a methodology for evaluating the usefulness of information and the privacy- preserving data publishing. We developing a case study and we provided a framework for measuring the privacy-preserving. Problems are exposed in the measurement of the usefulness of the data and relate to privacy-preserving data publishing. Machine learning models are developed to determine the risk of predicting sensitive attributes and as a means of verifying the usefulness of the data. The findings motivate the need to adapt the privacy measures to current requirements and sophisticated attacks as the machine learning.

: machine learning; privacy-preserving; data publishing; privacy measurement.

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