Differential privacy via t-closeness in data publishing

k-Anonymity and e-differential privacy are two main privacy models proposed within the computer science community. Whereas the former was proposed for privacy-preserving data publishing, i.e. data set anonymization, the latter initially arose in the context of interactive databases and was later extended to data publishing. We show here that t-closeness, one of the extensions of k-anonymity, can actually yieldε-differential privacy in data publishing when t =exp(ε). We detail a construction based on bucketization that realizes the previous implication; hence, as an ancillary result, we provide a new computational procedure to achieve t-closeness and ε-differential privacy in data publishing.