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Research Note ---Generating Shareable Statistical Databases for Business Value: Multiple Imputation with Multimodal Perturbation

Author

Listed:
  • Nigel Melville

    (Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

  • Michael McQuaid

    (School of Information, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

Business organizations are generating growing volumes of data about their employees, customers, and suppliers. Much of these data cannot be exploited for business value due to privacy and confidentiality concerns. National statistical agencies share sensitive data collected from individuals and businesses by modifying the data so individuals and firms cannot be identified but statistical utility is preserved. We build on this literature to develop a hybrid approach to data masking for business organizations. We demonstrate the validity of the hybrid approach, which we call multiple imputation with multimodal perturbation (MIMP), using Monte Carlo simulation and illustrate its application in a specific business context. Results of our analysis open new areas of research for information systems scholarship and new potential revenue sources for business organizations.

Suggested Citation

  • Nigel Melville & Michael McQuaid, 2012. "Research Note ---Generating Shareable Statistical Databases for Business Value: Multiple Imputation with Multimodal Perturbation," Information Systems Research, INFORMS, vol. 23(2), pages 559-574, June.
    • Handle: RePEc:inm:orisre:v:23:y:2012:i:2:p:559-574
    DOI: 10.1287/isre.1110.0361
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    References listed on IDEAS

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    1. Philip K. Hopke & Chuanhai Liu & Donald B. Rubin, 2001. "Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic," Biometrics, The International Biometric Society, vol. 57(1), pages 22-33, March.
    2. Krishnamurty Muralidhar & Rathindra Sarathy, 2006. "Data Shuffling--A New Masking Approach for Numerical Data," Management Science, INFORMS, vol. 52(5), pages 658-670, May.
    3. Jeffrey M. Perloff & Mark Denbaly, 2007. "Data Needs for Consumer and Retail Firm Studies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(5), pages 1282-1287.
    4. Robert Garfinkel & Ram Gopal & Steven Thompson, 2007. "Releasing Individually Identifiable Microdata with Privacy Protection Against Stochastic Threat: An Application to Health Information," Information Systems Research, INFORMS, vol. 18(1), pages 23-41, March.
    5. Jerome P. Reiter, 2005. "Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 185-205, January.
    6. Paass, Gerhard, 1988. "Disclosure Risk and Disclosure Avoidance for Microdata," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(4), pages 487-500, October.
    7. Syam Menon & Sumit Sarkar & Shibnath Mukherjee, 2005. "Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns," Information Systems Research, INFORMS, vol. 16(3), pages 256-270, September.
    8. Seokho Lee & Marc G. Genton & Reinaldo B. Arellano-Valle, 2010. "Perturbation of Numerical Confidential Data via Skew-t Distributions," Management Science, INFORMS, vol. 56(2), pages 318-333, February.
    9. Xiao-Bai Li & Sumit Sarkar, 2006. "Privacy Protection in Data Mining: A Perturbation Approach for Categorical Data," Information Systems Research, INFORMS, vol. 17(3), pages 254-270, September.
    10. Zhiqiang Zheng & Balaji Padmanabhan, 2006. "Selectively Acquiring Customer Information: A New Data Acquisition Problem and an Active Learning-Based Solution," Management Science, INFORMS, vol. 52(5), pages 697-712, May.
    11. C.J. Skinner, 1992. "On identification disclosure and prediction disclosure for microdata," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 46(1), pages 21-32, March.
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