| 来源类型 | Working Paper
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| 规范类型 | 报告
|
| DOI | 10.3386/w19586
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| 来源ID | Working Paper 19586
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| Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases |
| Yi Qian; Hui Xie
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| 发表日期 | 2013-10-24
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| 出版年 | 2013
|
| 语种 | 英语
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| 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
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| URL | https://www.nber.org/papers/w19586
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| 来源智库 | National Bureau of Economic Research (United States)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/577261
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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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