  Group based anonymization is the most widely studied approach for privacy
preserving data publishing. This includes k-anonymity, l-diversity, and
t-closeness, to name a few. The goal of this paper is to raise a fundamental
issue on the privacy exposure of the current group based approach. This has
been overlooked in the past. The group based anonymization approach basically
hides each individual record behind a group to preserve data privacy. If not
properly anonymized, patterns can actually be derived from the published data
and be used by the adversary to breach individual privacy. For example, from
the medical records released, if patterns such as people from certain countries
rarely suffer from some disease can be derived, then the information can be
used to imply linkage of other people in an anonymized group with this disease
with higher likelihood. We call the derived patterns from the published data
the foreground knowledge. This is in contrast to the background knowledge that
the adversary may obtain from other channels as studied in some previous work.
Finally, we show by experiments that the attack is realistic in the privacy
benchmark dataset under the traditional group based anonymization approach.
