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The Security of Confidential Numerical Data in Databases

Author

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  • Rathindra Sarathy

    (Department of Management, Oklahoma State University, Stillwater, Oklahoma 74078-4011)

  • Krishnamurty Muralidhar

    (School of Management, Gatton College of Business & Economics, University of Kentucky, Lexington, Kentucky 40506-0034)

Abstract

Organizations are storing large amounts of data in databases for data mining and other types of analysis. Some of this data is considered confidential and has to be protected from disclosure. When access to individual values of confidential numerical data in the database is prevented, disclosure may occur when a snooper uses linear models to predict individual values of confidential attributes using nonconfidential numerical and categorical attributes. Hence, it is important for the database administrator to have the ability to evaluate security for snoopers using linear models. In this study we provide a methodology based on Canonical Correlation Analysis that is both appropriate and adequate for evaluating security. The methodology can also be used to evaluate the security provided by different security mechanisms such as query restrictions and data perturbation. In situations where the level of security is inadequate, the methodology provided in this study can also be used to select appropriate inference control mechanisms. The application of the methodology is illustrated using a simulated database.

Suggested Citation

  • Rathindra Sarathy & Krishnamurty Muralidhar, 2002. "The Security of Confidential Numerical Data in Databases," Information Systems Research, INFORMS, vol. 13(4), pages 389-403, December.
    • Handle: RePEc:inm:orisre:v:13:y:2002:i:4:p:389-403
    DOI: 10.1287/isre.13.4.389.74
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
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    Cited by:

    1. 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.
    2. Syam Menon & Sumit Sarkar, 2007. "Minimizing Information Loss and Preserving Privacy," Management Science, INFORMS, vol. 53(1), pages 101-116, January.
    3. Sam Ransbotham & Sabyasachi Mitra, 2009. "Choice and Chance: A Conceptual Model of Paths to Information Security Compromise," Information Systems Research, INFORMS, vol. 20(1), pages 121-139, March.
    4. Joseph B. Kadane & Ramayya Krishnan & Galit Shmueli, 2006. "A Data Disclosure Policy for Count Data Based on the COM-Poisson Distribution," Management Science, INFORMS, vol. 52(10), pages 1610-1617, October.
    5. 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.

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