Nowadays many educational institutions crucially need to understand the dynamics at the basis of the university dropout (UD) phe- nomenon. However, the most informative educational data are personal and subject to strict privacy constraints. The challenge is therefore to develop a data driven system which accurately predicts students dropouts while preserving the privacy of individual data instances. In the present paper we investigate this problem, making use of data collected at Univer- sity of Genoa as a case study.
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