Perturbation of Numerical Confidential Data via Skew-t Distributions
提出一种基于偏斜t分布的新数据扰动方法，能处理实际数据中的偏斜和厚尾特征，并通过蒙特卡洛模拟验证其在保护数值数据库隐私方面的效果，尤其适用于乳腺癌医疗数据库。
We propose a new data perturbation method for numerical database security problems based on skew-t distributions. Unlike the normal distribution, the more general class of skew-t distributions is a flexible parametric multivariate family that can model skewness and heavy tails in the data. Because databases having a normal distribution are seldom encountered in practice, the newly proposed approach, coined the skew-t data perturbation (STDP) method, is of great interest for database managers. We also discuss how to preserve the sample mean vector and sample covariance matrix exactly for any data perturbation method. We investigate the performance of the STDP method by means of a Monte Carlo simulation study and compare it with other existing perturbation methods. Of particular importance is the ability of STDP to reproduce characteristics of the joint tails of the distribution in order for database users to answer higher-level questions. We apply the STDP method to a medical database related to breast cancer.