关于隐私保护直方图
On Privacy-Preserving Histograms
摘要：
我们提出的方法发起的Chawla等人。消毒（人口普查）数据，以保护受访者的隐私，同时提取“有用的”统计信息。首先，我们将他们的技术范围扩展到广泛而丰富的一类分布，特别是高维球，球体，高斯分布和其他“漂亮”分布的混合物。其次，我们随机化直方图构造以保留数据的空间特征，使我们能够近似各种感兴趣的量，例如，数据上的最小生成树的成本，以隐私保护的方式。
英文摘要：
We advance the approach initiated by Chawla et al. for sanitizing (census) data so as to preserve the privacy of respondents while simultaneously extracting "useful" statistical information. First, we extend the scope of their techniques to a broad and rich class of distributions, specifically, mixtures of highdimensional balls, spheres, Gaussians, and other "nice" distributions. Second, we randomize the histogram constructions to preserve spatial characteristics of the data, allowing us to approximate various quantities of interest, e.g., cost of the minimum spanning tree on the data, in a privacy-preserving fashion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
