DPSynthesizer: Differentially Private Data Synthesizer for Privacy Preserving Data Sharing.
DPSynthesizer: Differentially Private Data Synthesizer for Privacy Preserving Data Sharing.
复制标题
DOI：
10.14778/2733004.2733059
复制
发表时间：
2014-08
期刊：
影响因子：
--
通讯作者：
Jiang X
中科院分区：
文献类型：
--
作者：
Li H;Xiong L;Zhang L;Jiang X
差异隐私最近出现在私人统计数据发布中，作为最强的隐私保证之一。发布模拟具有差异隐私的原始数据的合成数据为隐私保护数据共享和分析提供了一种有前途的方法，同时提供了严格的隐私保证。然而，到目前为止，还没有允许用户生成差异化私有合成数据的开源工具，特别是对于高维和大型域数据。大多数现有的生成差异私有直方图或合成数据的技术仅适用于一维或低维直方图。由于扰动误差和计算复杂度的增加，它们对于高维和大域数据变得有问题。我们提出DPSynthesizer，一个工具包，用于差分私人数据合成。DPSynthesizer的核心是为高维和大域数据设计的DPCopula。DPCopula计算一个差分私有copula函数，可以从中采样合成数据。Copula函数用于描述多元随机向量之间的依赖关系，并允许我们使用一维边缘分布来构建多元联合分布。DPSynthesizer还实现了一套最先进的方法，用于构建适用于低维数据的差分私有直方图，从中可以生成合成数据。我们将使用DPCopula以及其他方法与各种数据集演示系统，并显示各种方法的可行性，实用性和效率。
Differential privacy has recently emerged in private statistical data release as one of the strongest privacy guarantees. Releasing synthetic data that mimic original data with Differential privacy provides a promising way for privacy preserving data sharing and analytics while providing a rigorous privacy guarantee. However, to this date there is no open-source tools that allow users to generate differentially private synthetic data, in particular, for high dimensional and large domain data. Most of the existing techniques that generate differentially private histograms or synthetic data only work well for single dimensional or low-dimensional histograms. They become problematic for high dimensional and large domain data due to increased perturbation error and computation complexity. We propose DPSynthesizer, a toolkit for differentially private data synthesization. The core of DPSynthesizer is DPCopula designed for high-dimensional and large-domain data. DPCopula computes a differentially private copula function from which synthetic data can be sampled. Copula functions are used to describe the dependence between multivariate random vectors and allow us to build the multivariate joint distribution using one-dimensional marginal distributions. DPSynthesizer also implements a set of state-of-the-art methods for building differentially private histograms, suitable for low-dimensional data, from which synthetic data can be generated. We will demonstrate the system using DPCopula as well as other methods with various data sets and show the feasibility, utility, and efficiency of various methods.