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来源类型Working Paper
规范类型报告
DOI10.3386/w19586
来源IDWorking Paper 19586
Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases
Yi Qian; Hui Xie
发表日期2013-10-24
出版年2013
语种英语
摘要Databases play a central role in evidence-based innovations in business, economics, social, and health sciences. In modern business and society, there are rapidly growing demands for constructing analytically valid databases that also are secure and protect sensitive information in order to meet customer and public expectations, to minimize financial losses, and to comply with privacy regulations and laws. We propose new data perturbation and shuffling (DPS) procedures, named MORE, for this purpose. As compared with existing DPS methods, MORE can substantially increase the utility of secure databases without increasing disclosure risk. MORE is capable of preserving important nonmonotonic relationships among attributes, such as the inverted-U relationship between competition and innovation. Maintaining such relationships is often the key to determining optimal levels of policy and managerial interventions. MORE does not require data to be of particular types or have particular distributional shapes. Instead, it provides unified, flexible, and robust algorithms to mask general types of confidential variables with arbitrary distributions, thereby making it suitable for general-purpose data masking. Since MORE nests the commonly used generalized linear models as special cases, a much wider range of statistical analyses can be conducted using the secure databases with results similar to those using the original databases. Unlike existing DPS approaches which typically require a joint model for all variables, MORE requires no modeling of nonconfidential variables, and thus further increases the robustness of secure databases. Evaluation of MORE through Monte Carlo simulation studies and empirical applications demonstrates that it performs better than existing data masking methods.
主题Other ; Accounting, Marketing, and Personnel
URLhttps://www.nber.org/papers/w19586
来源智库National Bureau of Economic Research (United States)
引用统计
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/577261
推荐引用方式
GB/T 7714
Yi Qian,Hui Xie. Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases. 2013.
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