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Controlled Rounding of Tabular Data

Author

Listed:
  • James P. Kelly

    (University of Maryland, College Park, Maryland)

  • Bruce L. Golden

    (University of Maryland, College Park, Maryland)

  • Arjang A. Assad

    (University of Maryland, College Park, Maryland)

  • Edward K. Baker

    (University of Miami, Coral Gables, Florida)

Abstract

Controlled rounding is a procedure that perturbs tabular data collected from respondents in such a way as to preserve the anonymity of the respondents while maintaining the integrity of the data. The three-dimensional controlled rounding problem is NP-complete. This paper describes a binary tree search algorithm for solving three-dimensional problems based on linear programming techniques. Computational results obtained from tests with over 31,000 randomly generated tables and 292 real-life tables have shown that this algorithm can effectively find controlled roundings when they exist or determine that no solution exists. The computational results also demonstrate that the running time of the search algorithm can be cut in half by using a heuristic for initializing the simplex basis for the linear programming problems.

Suggested Citation

  • James P. Kelly & Bruce L. Golden & Arjang A. Assad & Edward K. Baker, 1990. "Controlled Rounding of Tabular Data," Operations Research, INFORMS, vol. 38(5), pages 760-772, October.
    • Handle: RePEc:inm:oropre:v:38:y:1990:i:5:p:760-772
    DOI: 10.1287/opre.38.5.760
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    Citations

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    Cited by:

    1. George, John A. & Kuan, Chong Juin & Ring, Brendan J., 1995. "Confidentiality control of tabulated data: Some practical network models," European Journal of Operational Research, Elsevier, vol. 85(3), pages 454-472, September.
    2. Juan-José Salazar-González, 2005. "A Unified Mathematical Programming Framework for Different Statistical Disclosure Limitation Methods," Operations Research, INFORMS, vol. 53(5), pages 819-829, October.
    3. Sage, Andrew J. & Wright, Stephen E., 2016. "Obtaining cell counts for contingency tables from rounded conditional frequencies," European Journal of Operational Research, Elsevier, vol. 250(1), pages 91-100.
    4. 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.
    5. Castro, Jordi, 2006. "Minimum-distance controlled perturbation methods for large-scale tabular data protection," European Journal of Operational Research, Elsevier, vol. 171(1), pages 39-52, May.
    6. Ring, Brendan J. & George, John A. & Kuan, Chong Juin, 1997. "A fast algorithm for large-scale controlled rounding of 3-dimensional census tables," Socio-Economic Planning Sciences, Elsevier, vol. 31(1), pages 41-55, March.

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