Noiseless Database Privacy
Noiseless Database Privacy
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DOI：
10.1007/978-3-642-25385-0_12
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发表时间：
2011-12
期刊：
影响因子：
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通讯作者：
Raghav Bhaskar;Abhishek Bhowmick;Vipul Goyal;S. Laxman;Abhradeep Thakurta
中科院分区：
文献类型：
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作者：
Raghav Bhaskar;Abhishek Bhowmick;Vipul Goyal;S. Laxman;Abhradeep Thakurta
差分隐私（DP）已经成为一个正式的，灵活的隐私保护框架，保证是不可知的辅助信息，并承认简单的规则组成。尽管有很多好处，DP的一个主要缺点是它对查询提供了嘈杂的响应，使其不适合许多应用程序。我们提出了一个新的概念，称为无噪声隐私，提供准确的答案查询，没有添加任何噪音。虽然我们的保证形式类似于DP，但隐私来自哪里是非常不同的，基于对数据的统计假设和对对手可用的辅助信息的限制。我们提出了第一组结果的无噪声隐私的任意布尔函数查询和线性实函数查询，当数据是独立绘制的，分别从近均匀分布和高斯分布。我们还得到了简单的规则，组成下的动态变化的数据模型。
Differential Privacy (DP) has emerged as a formal, flexible framework for privacy protection, with a guarantee that is agnostic to auxiliary information and that admits simple rules for composition. Benefits notwithstanding, a major drawback of DP is that it provides noisy responses to queries, making it unsuitable for many applications. We propose a new notion called Noiseless Privacy that provides exact answers to queries, without adding any noise whatsoever. While the form of our guarantee is similar to DP, where the privacy comes from is very different, based on statistical assumptions on the data and on restrictions to the auxiliary information available to the adversary. We present a first set of results for Noiseless Privacy of arbitrary Boolean-function queries and of linear Real-function queries, when data are drawn independently, from nearly-uniform and Gaussian distributions respectively. We also derive simple rules for composition under models of dynamically changing data.