Data shuffling - A new masking approach for numerical data

• 成果类型: Article
• 署名作者: Muralidhar, Krishnamurty; Sarathy, Rathindra
• 署名单位: University of Kentucky; Oklahoma State University System; Oklahoma State University - Stillwater
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.1050.0503
• 发表日期: 2006
• 页码: 658-670
• 关键词: camouflage; CONFIDENTIALITY; data masking; data swapping; obfuscation; privacy; perturbation

摘要

This study discusses a new procedure for masking confidential numerical data-a procedure called data shuffling-in which the values of the confidential variables are shuffled among observations. The shuffled data provides a high level of data utility and minimizes the risk of disclosure. From a practical perspective, data shuffling overcomes reservations about using perturbed or modified confidential data because it retains all the desirable properties of perturbation methods and performs better than other masking techniques in both data utility and disclosure risk. In addition, data shuffling can be implemented using only rank-order data, and thus provides a nonparametric method for masking. We illustrate the applicability of data shuffling for small and large data sets.