  k-Anonymity and {\epsilon}-differential privacy are two mainstream privacy
models, the former introduced to anonymize data sets and the latter to limit
the knowledge gain that results from including one individual in the data set.
Whereas basic k-anonymity only protects against identity disclosure,
t-closeness was presented as an extension of k-anonymity that also protects
against attribute disclosure. We show here that, if not quite equivalent,
t-closeness and {\epsilon}-differential privacy are strongly related to one
another when it comes to anonymizing data sets. Specifically, k-anonymity for
the quasi-identifiers combined with {\epsilon}-differential privacy for the
confidential attributes yields stochastic t-closeness (an extension of
t-closeness), with t a function of k and {\epsilon}. Conversely, t-closeness
can yield {\epsilon}- differential privacy when t = exp({\epsilon}/2) and the
assumptions made by t-closeness about the prior and posterior views of the data
hold
