Perturbation of Numerical Confidential Data via Skew-t Distributions

• 成果类型: Article
• 署名作者: Lee, Seokho; Genton, Marc G.; Arellano-Valle, Reinaldo B.
• 署名单位: Harvard University; Harvard T.H. Chan School of Public Health; Texas A&M University System; Texas A&M University College Station; Pontificia Universidad Catolica de Chile
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.1090.1104
• 发表日期: 2010
• 页码: 318-333
• 关键词: confidentiality; database management; kurtosis; MULTIVARIATE; security; simulation; skewness

摘要

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.