Releasing Individually Identifiable Microdata with Privacy Protection Against Stochastic Threat: An Application to Health Information

References

• Adam N. R., Wortmann J. C. Security-control methods for statistical databases: A comparative study. ACM Comput. Surveys (1989) 21:515–556Crossref, Google Scholar
• Aggarwal C. On k-anonymity and the curse of dimensionality. Proc. 31st Internat. Conf. Very Large Databases (VLDB’05) (2005) Trondheim, NorwayGoogle Scholar
• Aggarwal C., Feder T., Kenthapadi K., Motwani R., Panigrahy R., Thomas D., Zhu A. Approximation algorithms for k-anonymity. J. Privacy Tech. (2005) Google Scholar
• Carvalho F. D., Dellaert N., Osorio M. S. Statistical disclosure in two-dimensional tables: General tables. J. Amer. Statist. Assoc. (1994) 89:1547–1557Crossref, Google Scholar
• Causey B. D., Cox L. H., Ernst L. R. Application of transportation thoery to statistical problems. J. Amer. Statist. Assoc. (1985) 80:909Crossref, Google Scholar
• CDC HIPAA privacy rule and public health: Guidance from CDC and the U.S. Department of Health and Human Services. (2005) . http://www.cdc.gov/mmwr/preview/mmwrhtml/m2e411a1.htmGoogle Scholar
• Chowdhury S. D., Duncan G. T., Krishnan R., Roehrig S. F., Mukherjee S. Disclosure detection in multivariate categorical databases: Auditing confidentiality protection through two new matrix operators. Management Sci. (1999) 45:1710–1723Link, Google Scholar
• Cox L. H. Suppression methodology and statistical disclosure control. J. Amer. Statist. Assoc. (1980) 75:377–385Crossref, Google Scholar
• Cox L. H. Solving confidentiality protection problems in tabulations using network optimization: A network model for cell suppression in U.S. economic censuses. Internat. Sem. Statist. Confidentiality (1992) (Eurostat, Dublin, Ireland) 229–245Google Scholar
• Dobra A., Fienberg S. E., Trottini M., Bernardo J. M., Bayarri M. J., Berger J. O., Dawid A. P., Heckerman D., Smith A. F. M., West M. Assessing the risk of disclosure of confidential categorical data. Bayesian Statistics 7 (2003) (Oxford University Press, Oxford, UK) 125–144Google Scholar
• Domingo-Ferrer J., Mateo-Sanz J. M. Practical data-oriented microaggregation for statistical disclosure control. IEEE Trans. Knowledge Data Engrg. (2002) 14(1):189–201Crossref, Google Scholar
• Domingo-Ferrer D., Torra V. Ordinal, continuous, and heterogeneous k-anonymity through microaggregation. Data Mining Knowledge Discovery (2005) 11(2):195–212Crossref, Google Scholar
• Duncan G. T., Fienberg S., Domingo-Ferrer J. Obtaining information while preserving privacy: A Markov perturbation method for tabular data. Statist. Data Protection (SDP 1998) Proc. (1999) (IDS Press, Amsterdam, The Netherlands) Google Scholar
• Duncan G., Krishnan R., Padman R., Reuther P., Roehrig S. Exact and heuristic methods for cell suppression in multi-dimensional linked tables. (2003a) . Working paper, Carnegie Mellon University, Pittsburgh, PAGoogle Scholar
• Duncan G. T., Keller-McNulty S., Stokes S. L. Disclosure risk vs. data utility: The R-U confidentiality map. (2003b) . Technical Report 2003-6, Heinz School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, PAGoogle Scholar
• Fischetti M., Salazar J. Solving the cell suppression on tabular data with linear constraints. Management Sci. (2001) 47:1008–1027Link, Google Scholar
• Garfinkel R., Gopal R. D., Goes P. B. Privacy protection of binary confidential data against deterministic, stochastic, and insider threat. Management Sci. (2002) 48:749–764Link, Google Scholar
• Gedik B., Liu L. A customizable k-anonymity model for protecting location privacy. Proc. 25th Internat. Conf. Distributed Comput. Systems (IEEE ICDCS) (2005) Columbus, OHGoogle Scholar
• Geurts J. Heuristics for cell suppression in tables. (1992) . Working paper, Central Bureau of Statistics, The NetherlandsGoogle Scholar
• Gopal R. D., Goes P. B., Garfinkel R. Interval protection of confidential information in a database. INFORMS J. Comput. (1998) 10:309–322Link, Google Scholar
• Health Insurance Portability and Accountability Act (HIPAA) (1996) Google Scholar
• Hundepool A., Willenborg L. μ- and τ-ARGUS: Software for statistical disclosure control. Proc. 3rd Internat. Seminar Statist. Confidentiality (1996) Bled, SloveniaGoogle Scholar
• Jiang W., Clifton C. Privacy-preserving distributed k-anonymity. Proc. 19th Annual IFIP WG 11.3 Working Conf. Data Appl. Security (2005) Storrs, CTCrossref, Google Scholar
• Kelly J. P. Confidentiality protection in two and three-dimensional tables. (1990) . Unpublished doctoral dissertation, University of Maryland, College Park, MDGoogle Scholar
• Li X., Sarkar S. Privacy protection in data mining: A perturbation approach for categorical data. Inform. Systems Res. (2006a) 17(3):254–270Link, Google Scholar
• Li X., Sarkar S. A tree-based perturbation approach for privacy-preserving data mining. IEEE Trans. Knowledge Data Engrg. (2006b) 18(9):1278–1283Crossref, Google Scholar
• Menon S., Sarkar S., Mukherjee S. Maximizing accuracy of shared databases when concealing sensitive patterns. Inform. Systems Res. (2005) 16(3):256–270Link, Google Scholar
• Meyerson A., Williams R. On the complexity of optimal k-anonymity. Proc. 23rd ACM-SIGMOD-SIGACT-SIGART Sympos. Principles Database Systems (2004) Paris, France:223–228Crossref, Google Scholar
• Muralidhar K., Batra D., Kirs P. J. Accessibility, security, and accuracy in statistical databases: The case for the multiplicative fixed data perturbation approach. Management Sci. (1995) 41:1549–1564Link, Google Scholar
• Newton E., Sweeney L., Malin B. Preserving privacy by de-identifying facial images. IEEE Trans. Knowledge Data Engrg. (2005) 17(2):232–243Crossref, Google Scholar
• Sweeney L. Guaranteeing anonymity when sharing medical data. Proc. J. Amer. Medical Inform. Assoc. (2002a) (Hanley & Belfus, Inc., Washington, D.C.) Google Scholar
• Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression. Internat. J. Uncertainty, Fuzziness Knowledge-Based Systems (2002b) 10(5):571–588Crossref, Google Scholar
• United States General Accounting Office Report to congressional requesters: Medical records privacy. (1999) FebruaryGoogle Scholar
• Willenborg L., De Waal A.Statistical Disclosure Control in Practice (1996) (Springer-Verlag, New York) Crossref, Google Scholar
• Willenborg L., Hundepool A., Domingo-Ferrer J. ARGUS for statistical disclosure control. Statist. Data Protection (SDP 1998) Proc. (1998) (IDS Press, Amsterdam, The Netherlands) Google Scholar
• Zayatz L. Using linear programming methodology for disclosure avoidance purposes. (1992) . Research report, Statistical Research Division, Bureau of the Census, Washington, D.C.Google Scholar