论文标题
通过矩阵掩盖降低差异隐私的噪声水平
Reducing Noise Level in Differential Privacy through Matrix Masking
论文作者
论文摘要
近年来，已广泛采用差异隐私计划来解决数据隐私保护问题。我们提出了一种新的高斯方案，结合了另一种称为随机正交矩阵掩模的数据保护技术，以更有效地实现$（\ varepsilon，δ）$ - 差异隐私（DP）。我们证明，额外的矩阵掩蔽可显着降低高斯方案中所需的噪声方差速率，以实现$（\ varepsilon，δ） - 在大数据设置中$ dp。具体而言，当$ \ varepsilon \ to 0 $，$δ\至0 $，样本大小$ n $超过$（n-p）= o（ln（1/δ）$的数字$ p $ p $），所需的附加噪声差异可实现$（\ varepsilon，δ）$ -dp $（\ vareps $/dp）$（ln $ o（1）至$ o（1/\ varepsilon）$。随着噪声的添加得多，由此产生的差异隐私保护伪数据集可以更准确地推断，因此可以显着提高差异隐私应用程序的应用范围。
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 $(\varepsilon, δ)$-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 $(\varepsilon, δ)-$DP in big data setting. Specifically, when $\varepsilon \to 0$, $δ\to 0$, and the sample size $n$ exceeds the number $p$ of attributes by $(n-p)=O(ln(1/δ))$, the required additive noise variance to achieve $(\varepsilon, δ)$-DP is reduced from $O(ln(1/δ)/\varepsilon^2)$ 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.
