  Disassociation introduced by Terrovitis et al. is a bucketization based
anonimyzation technique that divides a set-valued dataset into several clusters
to hide the link between individuals and their complete set of items. It
increases the utility of the anonymized dataset, but on the other side, it
raises many privacy concerns, one in particular, is when the items are tightly
coupled to form what is called, a cover problem. In this paper, we present safe
disassociation, a technique that relies on partial-suppression, to overcome the
aforementioned privacy breach encountered when disassociating set-valued
datasets. Safe disassociation allows the $k^m$-anonymity privacy constraint to
be extended to a bucketized dataset and copes with the cover problem. We
describe our algorithm that achieves the safe disassociation and we provide a
set of experiments to demonstrate its efficiency.
