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Accessibility, Security, and Accuracy in Statistical Databases: The Case for the Multiplicative Fixed Data Perturbation Approach

Author

Listed:
  • Krishnamurty Muralidhar

    (Florida International University, Department of Decision Sciences and Information Systems, University Park, Miami, Florida 33199)

  • Dinesh Batra

    (Florida International University, Department of Decision Sciences and Information Systems, University Park, Miami, Florida 33199)

  • Peeter J. Kirs

    (Department of Management, University of Texas at El Paso, El Paso, Texas 77968)

Abstract

Organizations store data regarding their operations, employees, consumers, and suppliers in their databases. Some of the data are considered confidential, and by law, the organization is required to provide appropriate security measures in order to preserve privacy. Yet a number of companies have little or no security measures. The reason for this lack of security may, at least in part, be attributed to a lack of awareness and empirical evidence about the relative effectiveness of security mechanisms. This study investigates the effectiveness of different security mechanisms for protecting numerical database attributes. The trade-off between security, accessibility, and accuracy are examined. A comparison of different security mechanisms reveals that fixed data perturbation is preferred because it maximizes both security and accessibility. An investigation of the different approaches to fixed data perturbation indicates that multiplicative method best meets these criteria.

Suggested Citation

  • Krishnamurty Muralidhar & Dinesh Batra & Peeter J. Kirs, 1995. "Accessibility, Security, and Accuracy in Statistical Databases: The Case for the Multiplicative Fixed Data Perturbation Approach," Management Science, INFORMS, vol. 41(9), pages 1549-1564, September.
    • Handle: RePEc:inm:ormnsc:v:41:y:1995:i:9:p:1549-1564
    DOI: 10.1287/mnsc.41.9.1549
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    Citations

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    Cited by:

    1. S F Roehrig & R Padman & R Krishnan & G T Duncan, 2011. "Exact and heuristic methods for cell suppression in multi-dimensional linked tables," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(2), pages 291-304, February.
    2. Manuel A. Nunez & Robert S. Garfinkel & Ram D. Gopal, 2007. "Stochastic Protection of Confidential Information in Databases: A Hybrid of Data Perturbation and Query Restriction," Operations Research, INFORMS, vol. 55(5), pages 890-908, October.
    3. Yi Qian & Hui Xie, 2013. "Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases," NBER Working Papers 19586, National Bureau of Economic Research, Inc.
    4. Ram D. Gopal & Paulo B. Goes & Robert S. Garfinkel, 1998. "Interval Protection of Confidential Information in a Database," INFORMS Journal on Computing, INFORMS, vol. 10(3), pages 309-322, August.
    5. Sumit Dutta Chowdhury & George T. Duncan & Ramayya Krishnan & Stephen F. Roehrig & Sumitra Mukherjee, 1999. "Disclosure Detection in Multivariate Categorical Databases: Auditing Confidentiality Protection Through Two New Matrix Operators," Management Science, INFORMS, vol. 45(12), pages 1710-1723, December.
    6. Soon‐Young Huh & Kyoung‐Ll Bae, 1999. "Dynamic web server construction on the intranet using a change management framework," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 8(1), pages 45-60, March.
    7. Han Li & Krishnamurty Muralidhar & Rathindra Sarathy, 2007. "Technical Note---Assessment of Disclosure Risk When Using Confidentiality via Camouflage," Operations Research, INFORMS, vol. 55(6), pages 1178-1182, December.
    8. Heng Xu & Nan Zhang, 2022. "Implications of Data Anonymization on the Statistical Evidence of Disparity," Management Science, INFORMS, vol. 68(4), pages 2600-2618, April.
    9. Chu, Amanda M.Y. & Ip, Chun Yin & Lam, Benson S.Y. & So, Mike K.P., 2022. "Vine copula statistical disclosure control for mixed-type data," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    10. Rathindra Sarathy & Krishnamurty Muralidhar & Rahul Parsa, 2002. "Perturbing Nonnormal Confidential Attributes: The Copula Approach," Management Science, INFORMS, vol. 48(12), pages 1613-1627, December.
    11. 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.
    12. 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.
    13. Rathindra Sarathy & Krishnamurty Muralidhar, 2002. "The Security of Confidential Numerical Data in Databases," Information Systems Research, INFORMS, vol. 13(4), pages 389-403, December.
    14. Ram Gopal & Robert Garfinkel & Paulo Goes, 2002. "Confidentiality via Camouflage: The CVC Approach to Disclosure Limitation When Answering Queries to Databases," Operations Research, INFORMS, vol. 50(3), pages 501-516, June.
    15. Krishnamurty Muralidhar & Rahul Parsa & Rathindra Sarathy, 1999. "A General Additive Data Perturbation Method for Database Security," Management Science, INFORMS, vol. 45(10), pages 1399-1415, October.
    16. Yi Qian & Hui Xie, 2015. "Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases," Management Science, INFORMS, vol. 61(3), pages 520-541, March.
    17. Haibing Lu & Jaideep Vaidya & Vijayalakshmi Atluri & Yingjiu Li, 2015. "Statistical Database Auditing Without Query Denial Threat," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 20-34, February.

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