论文标题
改善使用Pufferfish隐私的相关列的隐私性分析的实用程序
Improving Utility for Privacy-Preserving Analysis of Correlated Columns using Pufferfish Privacy
论文作者
论文摘要
调查是社会科学研究许多领域的重要工具，但是隐私问题可能会使调查数据的收集和分析变得复杂。私人对调查数据的分析可以解决这些问题，但要以准确性为代价 - 尤其是对于高维统计。我们提出了一种新颖的隐私机制，即表格DDP机制，该机制专为具有不完全相关性的高维统计数据而设计。表格DDP机制满足了依赖的差异隐私，这是河豚隐私的变体；它通过构建敏感数据的因果模型，然后将噪声校准到统计之间的相关水平来起作用。对调查数据的经验评估表明，表格DDP机制可以显着提高与拉普拉斯机制的准确性。
Surveys are an important tool for many areas of social science research, but privacy concerns can complicate the collection and analysis of survey data. Differentially private analyses of survey data can address these concerns, but at the cost of accuracy - especially for high-dimensional statistics. We present a novel privacy mechanism, the Tabular DDP Mechanism, designed for high-dimensional statistics with incomplete correlation. The Tabular DDP Mechanism satisfies dependent differential privacy, a variant of Pufferfish privacy; it works by building a causal model of the sensitive data, then calibrating noise to the level of correlation between statistics. An empirical evaluation on survey data shows that the Tabular DDP Mechanism can significantly improve accuracy over the Laplace mechanism.
