Data Shuffling—A New Masking Approach for Numerical Data

Published Online:https://doi.org/10.1287/mnsc.1050.0503

References

  • Adam N. R., Wortmann J. C. Security-control methods for statistical databases: A comparative study. ACM Comput. Surveys (1989) 21:515–556CrossrefGoogle Scholar
  • Burridge J. Information preserving statistical obfuscation. Statist. Comput. (2003) 13:321–327CrossrefGoogle Scholar
  • Carlson M., Salabasis M. A data swapping technique for generating synthetic samples: A method for disclosure control. Res. Official Statist. (2002) 6:35–64Google Scholar
  • Clemen R. T., Reilly T. Correlations and copulas for decision and risk analysis. Management Sci. (1999) 45:208–224LinkGoogle Scholar
  • Dalenius T. Towards a methodology for statistical disclosure control. Statistisktidskrift (1977) 5:429–444Google Scholar
  • Dalenius T., Reiss S. P. Data-swapping: A technique for disclosure control. J. Statist. Planning Inference (1982) 6:73–85CrossrefGoogle Scholar
  • Dandekar R. A., Cohen M., Kirkendall N., Domingo-Ferrer J. Sensitive microdata protection using Latin hypercube sampling technique. Inference Control in Statistical Databases (2002) (Springer-Verlag, New York) CrossrefGoogle Scholar
  • Duncan G. T., Lambert D. Disclosure-limited data dissemination. J. Amer. Statist. Assoc. (1986) 81:10–18CrossrefGoogle Scholar
  • Duncan G. T., Pearson R. W. Enhancing access to microdata while protecting confidentiality: Prospects for the future. Statist. Sci. (1991) 6:219–239CrossrefGoogle Scholar
  • Duncan G. T., Keller-McNulty S. A., Stokes S. L. Disclosure risk vs. data utility: The R-U confidentiality map. (2001) . Technical report LA-UR-01-6428, Los Alamos National Laboratory, Los Alamos, NMGoogle Scholar
  • Fienberg S. E. Comment on paper by Carlson and Salabasis: “A data swapping technique for generating synthetic samples: A method for disclosure control.”. Res. Official Statist. (2002) 6:65–67Google Scholar
  • Fienberg S. E., McIntyre J. Data swapping: Variations on a theme by Dalenius and Reiss. J. Official Statist. (2005) 21:309–323Google Scholar
  • Fienberg S. E., Makov U. E., Sanil A. P. A Bayesian approach to data disclosure: Optimal intruder behavior for continuous data. J. Official Statist. (1997) 13:75–89Google Scholar
  • Fienberg S. E., Makov U. E., Steele A. P. Disclosure limitation using perturbation and related methods for categorical data. J. Official Statist. (1998a) 14:485–502Google Scholar
  • Fienberg S. E., Makov U. E., Steele A. P. Rejoinder. J. Official Statist. (1998b) 14:509–511Google Scholar
  • Fuller W. A. Masking procedures for microdata disclosure limitation. J. Official Statist. (1993) 9:383–406Google Scholar
  • Gopal R., Garfinkel R., Goes P. Confidentiality via camouflage: The CVC approach to disclosure limitation when answering queries to databases. Oper. Res. (2002) 50:501–516LinkGoogle Scholar
  • Kooiman P. Comment. J. Official Statist. (1998) 14:503–508Google Scholar
  • Liew C. K., Choi U. J., Liew C. J. A data distortion by probability distribution. ACM Trans. Database Systems (1985) 10:395–411CrossrefGoogle Scholar
  • Little R. J. A. Statistical analysis of masked data. J. Official Statist. (1993) 9:407–426Google Scholar
  • Moore R. A. Controlled data swapping for masking public use microdata sets. (1996) . Research report series no. RR96/04, U.S. Census Bureau, Statistical Research Division, Washington, D.C.Google Scholar
  • Muralidhar K., Sarathy R. A theoretical basis for perturbation methods. Statist. Comput. (2003a) 13:329–335CrossrefGoogle Scholar
  • Muralidhar K., Sarathy R. A rejoinder to the comments by Polettini and Stander. Statist. Comput. (2003b) 13:339–342CrossrefGoogle Scholar
  • Muralidhar K., Parsa R., Sarathy R. A general additive data perturbation method for database security. Management Sci. (1999) 45:1399–1415LinkGoogle Scholar
  • Muralidhar K., Sarathy R., Parsa R. An improved security requirement for data perturbation with implications for e-commerce. Decision Sci. (2001) 32:683–698CrossrefGoogle Scholar
  • Nelsen R. B.An Introduction to Copulas (1999) (Springer, New York) CrossrefGoogle Scholar
  • Raghunathan T. E., Reiter J. P., Rubin D. B. Multiple imputation for statistical disclosure limitation. J. Official Statist. (2003) 19:1–6Google Scholar
  • Reiss S. P., Post M. J., Dalenius T. Non-reversible privacy transformations. Proc. ACM Sympos. Principles Database Systems (1982) Los Angeles, CA:139–146CrossrefGoogle Scholar
  • Rubin D. B. Discussion of statistical disclosure limitation. J. Official Statist. (1993) 9:461–468Google Scholar
  • Sarathy R., Muralidhar K., Parsa R. Perturbing non-normal confidential variables: The copula approach. Management Sci. (2002) 48:1613–1627LinkGoogle Scholar
  • Wall Street Journal (2001) . Bureau blurs data to keep names confidential. (February 14) B1–B2Google Scholar
  • Willenborg L., de Waal T.Elements of Statistical Disclosure Control (2001) (Springer, New York) CrossrefGoogle Scholar
  • Winkler W. E. Advanced methods for record linkage. Proc. Survey Res. Methods Section (1995a) (American Statistical Association, Alexandria, VA) Google Scholar
  • Winkler W. E., Cox B. G. Matching and record linkage. Business Survey Methods (1995b) (John Wiley and Sons, New York) 355–384CrossrefGoogle Scholar
  • Winkler W. E. Producing public-user microdata that are analytically valid and confidential. (1998) . Statistical research report series no. RR98/02, U.S. Census Bureau, Statistical Research Division, Washington, D.C.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.