Abstract
s are used. Privacy Preserving Data Mining
(PPDM) algorithms have been recently introduced with the aim of mod-
ifying the database in such a way to prevent the discovery of sensible
information. Due to the large amount of possible techniques that can be
used to achieve this goal, it is necessary to provide some standard evalu-
ation metrics to determine the best algorithms for a specific application
or context. Currently, however, there is no common set of parameters
that can be used for this purpose. This paper explores the problem of
PPDM algorithm evaluation, starting from the key goal of preserving of
data quality. To achieve such goal, we propose a formal definition of data
quality specifically tailored for use in the context of PPDM algorithms, a
set of evaluation parameters and an evaluation algorithm. The resulting
evaluation core process is then presented as a part of a more general three
step evaluation framework, taking also into account other aspects of the
algorithm evaluation such as efficiency, scalability and level of privacy.
