Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases

Abstract

D atabases 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 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 by using the secure databases with results similar to those achieved by using the original databases. Unlike existing DPS approaches that 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.Data, as supplemental material, are available at http://dx.

Tutorial: Assessing the impact of nonignorable missingness on regression analysis using Index of Local Sensitivity to Nonignorability.

“…They enable analyses of incomplete data sets in regression analysis (H. Y. Chen et al, 2011; Enders et al, 2020; Lüdtke et al, 2020; Qian & Xie, 2011; Schafer & Graham, 2002), matched sampling and propensity score analysis (Cham & West, 2016; Qian, 2007), moderation analysis (Zhang & Wang, 2017), structural equation modeling (Lee & Shi, 2021), data fusion (Qian & Xie, 2014), data privacy and disclosure control (Qian & Xie, 2015), and other applications.…”

Elliptical modeling and pattern analysis for perturbation models and classification

“…As a consequence, the current techniques may not perform as desired. A thorough review was presented in a recent paper by (Qian and Xie, 2015), in which the authors summarized the possible types of violations of parametric assumptions, including uncertainty in marginal distributional properties of independent variables and possible nonlinear relationship that linear models cannot fully explore (e.g., invert-U shape (Aghion et al, 2005)). -highlighted using Green color for the parameters (a, b, α), respectively.…”