论文标题
统一的私人贝叶斯点估计方法
A Unified Approach to Differentially Private Bayes Point Estimation
论文作者
论文摘要
统计和系统识别中的参数估计取决于可能包含敏感信息的数据。为了保护这些敏感信息，已经提出了\ emph {dixial隐私}（DP）的概念，该概念通过在估计值中引入随机化来实施机密性。差异私有估计的标准算法基于向传统点估计方法的输出添加适当的噪声。这导致了准确的私人关系权衡，因为增加了更多的噪音在增加隐私的同时降低了准确性。在本文中，我们提出了一种新的统一贝叶斯私人点（UBAPP）方法，以在DP约束下对数据生成机制的未知参数进行贝叶斯点估计，这比传统方法实现了更好的准确性私人关系。我们在一个简单的数值示例中验证方法的性能。
Parameter estimation in statistics and system identification relies on data that may contain sensitive information. To protect this sensitive information, the notion of \emph{differential privacy} (DP) has been proposed, which enforces confidentiality by introducing randomization in the estimates. Standard algorithms for differentially private estimation are based on adding an appropriate amount of noise to the output of a traditional point estimation method. This leads to an accuracy-privacy trade off, as adding more noise reduces the accuracy while increasing privacy. In this paper, we propose a new Unified Bayes Private Point (UBaPP) approach to Bayes point estimation of the unknown parameters of a data generating mechanism under a DP constraint, that achieves a better accuracy-privacy trade off than traditional approaches. We verify the performance of our approach on a simple numerical example.
