Differential privacy schemes have been widely adopted in recent years to address issues of data privacy protection. We propose a new Gaussian scheme combining with another data protection technique, called random orthogonal matrix masking, to achieve $(\varepsilon, \delta)$-differential privacy (DP) more efficiently. We prove that the additional matrix masking significantly reduces the rate of noise variance required in the Gaussian scheme to achieve $(\varepsilon, \delta)-$DP in big data setting. Specifically, when $\varepsilon \to 0$, $\delta \to 0$, and the sample size $n$ exceeds the number $p$ of attributes by $\frac{n}{p}=O(ln(1/\delta))$, the required additive noise variance to achieve $(\varepsilon, \delta)$-DP is reduced from $O(ln(1/\delta)/\varepsilon^2)$ to $O(1/\varepsilon)$. With much less noise added, the resulting differential privacy protected pseudo data sets allow much more accurate inferences, thus can significantly improve the scope of application for differential privacy.

翻译：近年来,为了解决数据隐私保护问题,我们广泛采用了不同的隐私计划。我们提议了一个新的高斯计划,结合另一种数据保护技术,称为随机正方位矩阵遮罩,以更有效地实现$(varepsilon,\delta)美元差异隐私(DP),我们证明额外的矩阵掩盖大大降低了高斯计划为在大数据设置中实现$(varepsilon,\delta)-$DP所需的噪音差异率。具体来说,当美元(varepsilon)至0美元、美元(delta)至0美元、样本大小超过美元($)的属性数(p$)时,所需的添加式噪声变化从美元(n(1/delta)/\varepslon)美元降至美元($(1/\varepislon),从而大大降低隐私保护范围,从而大大改进了隐私应用的精确度。