  In recent years, there has been an exponential growth of applications,
including dialogue systems, that handle sensitive personal information. This
has brought to light the extremely important issue of personal data protection
in virtual environments. Sensitive Information Detection (SID) approaches
different domains and languages in literature. However, if we refer to the
personal data domain, a shared benchmark or the absence of an available labeled
resource makes comparison with the state-of-the-art difficult. We introduce and
release SPeDaC , a new annotated resource for the identification of sensitive
personal data categories in the English language. SPeDaC enables the evaluation
of computational models for three different SID subtasks with increasing levels
of complexity. SPeDaC 1 regards binary classification, a model has to detect if
a sentence contains sensitive information or not; whereas, in SPeDaC 2 we
collected labeled sentences using 5 categories that relate to macro-domains of
personal information; in SPeDaC 3, the labeling is fine-grained (61 personal
data categories). We conduct an extensive evaluation of the resource using
different state-of-the-art-classifiers. The results show that SPeDaC is
challenging, particularly with regard to fine-grained classification. The
transformer models achieve the best results (acc. RoBERTa on SPeDaC 1 = 98.20%,
DeBERTa on SPeDaC 2 = 95.81% and SPeDaC 3 = 77.63%).
