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 ext...

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Bibliographic Details
Main Authors: Soria-Comas, Jordi, Domingo-Ferrert, Josep
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Data privacy
Hypertension
Obesity
Pain
Privacy
Publishing
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Summary: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.
DOI:10.1109/PST.2013.6596033