  We explore some novel connections between the main privacy models in use and
we recall a few known ones. We show these models to be more related than
commonly understood, around two main principles: deniability and permutation.
In particular, randomized response turns out to be very modern in spite of it
having been introduced over 50 years ago: it is a local anonymization method
and it allows understanding the protection offered by $\epsilon$-differential
privacy when $\epsilon$ is increased to improve utility. A similar
understanding on the effect of large $\epsilon$ in terms of deniability is
obtained from the connection between $\epsilon$-differential privacy and
t-closeness. Finally, the post-randomization method (PRAM) is shown to be
viewable as permutation and to be connected with randomized response and
differential privacy. Since the latter is also connected with t-closeness, it
follows that the permutation principle can explain the guarantees offered by
all those models. Thus, calibrating permutation is very relevant in
anonymization, and we conclude by sketching two ways of doing it.
