Differential privacy via t-closeness in data publishing
Differential privacy via t-closeness in data publishing
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DOI：
10.1109/pst.2013.6596033
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
Jordi Soria-Comas;J. Domingo-Ferrer
中科院分区：
文献类型：
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作者：
Jordi Soria-Comas;J. Domingo-Ferrer
k-匿名和电子差分隐私是计算机科学界提出的两种主要隐私模型。前者是为了保护隐私的数据发布而提出的，即数据集匿名化，而后者最初是在交互式数据库的背景下出现的，后来扩展到数据发布。我们在这里表明，t-紧密性是 k-匿名性的扩展之一，当 t =exp(ε) 时，实际上可以在数据发布中产生 ε-差分隐私。我们详细介绍了一种基于分桶化的构造，它实现了前面的含义；因此，作为辅助结果，我们提供了一种新的计算过程来实现数据发布中的 t-紧密性和 ε-差分隐私。
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.