  This paper proposes the notion of 'Privacy-Anomaly Detection' and considers
the question of whether behavioural-based anomaly detection approaches can have
a privacy semantic interpretation and whether the detected anomalies can be
related to the conventional (formal) definitions of privacy semantics such as
k-anonymity. The idea is to learn the user's past querying behaviour in terms
of privacy and then identifying deviations from past behaviour in order to
detect privacy violations. Privacy attacks, violations of formal privacy
definition, based on a sequence of SQL queries (query correlations) are also
considered in the paper and it is shown that interactive querying settings are
vulnerable to privacy attacks based on query sequences. Investigation on
whether these types of privacy attacks can potentially manifest themselves as
anomalies, specifically as privacy-anomalies was carried out. It is shown that
in this paper that behavioural-based anomaly detection approaches have the
potential to detect privacy attacks based on query sequences (violation of
formal privacy definition) as privacy-anomalies.
