Data sharing and dissemination are becoming
increasingly important for conducting our daily life
activities. The main consequence of this trend is that
huge collections of data are easily available and
accessible,
leading to growing privacy concerns. The research
community has devoted many efforts aiming at
address-
ing the complex privacy requirements that characterize
the modern Information Society. Although several
advancements have been made, still many open issues
need to be investigated.
In this paper, we consider a scenario where data are
incrementally released and we address the privacy
problem arising when sensitive non released
properties depend on (and can therefore be inferred
from) non-
sensitive released data. We propose a model capturing
this inference problem, where sensitive information
is characterized by peculiar value distributions of non
sensitive released data. We then describe how to
counteract possible inferences that an observer can
draw by applying different statistical metrics