论文标题
有限的高斯差异隐私机制
The Bounded Gaussian Mechanism for Differential Privacy
论文作者
论文摘要
高斯机制是一种用于保护数值数据的差异隐私机制。但是，它可能不适合某些应用，因为它具有无限的支持，因此可以对查询产生无效的数值答案，例如数十米的负年龄或人类高度。人们可以将这种私人价值投射到有效的数据范围内，尽管这种预测导致在此类范围的边界上积累了私人查询响应，从而损害了准确性。由于需要对有限域的隐私和准确性的需要，我们提出了一种有限的高斯差异隐私机制，该机制仅在给定的区域支持。我们介绍了该机制的单变量和多元版本，并说明了相对于现有工作的可比工作，方差显着降低。
The Gaussian mechanism is one differential privacy mechanism commonly used to protect numerical data. However, it may be ill-suited to some applications because it has unbounded support and thus can produce invalid numerical answers to queries, such as negative ages or human heights in the tens of meters. One can project such private values onto valid ranges of data, though such projections lead to the accumulation of private query responses at the boundaries of such ranges, thereby harming accuracy. Motivated by the need for both privacy and accuracy over bounded domains, we present a bounded Gaussian mechanism for differential privacy, which has support only on a given region. We present both univariate and multivariate versions of this mechanism and illustrate a significant reduction in variance relative to comparable existing work.
