Coupled-Worlds Privacy: Exploiting Adversarial Uncertainty in Statistical Data Privacy
Coupled-Worlds Privacy: Exploiting Adversarial Uncertainty in Statistical Data Privacy
复制标题
耦合世界隐私：利用统计数据隐私中的对抗性不确定性
DOI：
10.1109/focs.2013.54
复制
发表时间：
2013
期刊：
影响因子：
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通讯作者：
Adam D. Smith
中科院分区：
文献类型：
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作者：
Raef Bassily;Adam Groce;Jonathan Katz;Adam D. Smith
我们提出了一个新的框架来定义统计数据库中的隐私，使推理和利用数据的对抗性不确定性。粗略地说，我们的框架要求真实世界的不可区分性，其中一个机制是在真实数据集上计算的，而理想世界中，模拟器输出数据集的“擦洗”版本的某些函数（例如，其中一个用户的数据被删除）。在每个世界中，底层数据集都是从某些类（作为定义的一部分指定）的相同分布中提取的，这模拟了对手对数据集的不确定性。我们认为，与之前在存在对抗性不确定性的情况下对隐私进行建模的努力相比，我们的框架在更广泛的环境中提供了有意义的保证。我们还表明，在对底层数据分布的现实假设下，几种自然的“无噪声”机制满足我们的定义框架。
We propose a new framework for defining privacy in statistical databases that enables reasoning about and exploiting adversarial uncertainty about the data. Roughly, our framework requires indistinguishability of the real world in which a mechanism is computed over the real dataset, and an ideal world in which a simulator outputs some function of a "scrubbed" version of the dataset (e.g., one in which an individual user's data is removed). In each world, the underlying dataset is drawn from the same distribution in some class (specified as part of the definition), which models the adversary's uncertainty about the dataset. We argue that our framework provides meaningful guarantees in a broader range of settings as compared to previous efforts to model privacy in the presence of adversarial uncertainty. We also show that several natural, "noiseless" mechanisms satisfy our definitional framework under realistic assumptions on the distribution of the underlying data.