Disclosure Detection in Multivariate Categorical Databases: Auditing Confidentiality Protection Through Two New Matrix Operators

References

• Adam N. R., Wortman J. C. Security-control methods for statistical databases: A comparative study. ACM Comput. Surveys (1989) 21:515–556Crossref, Google Scholar
• Barquin R., Edelstein H.Planning and Designing the Data Warehouse (1997) (Prentice Hall, New York) Google Scholar
• Carvalho F. D., Dellaert N., Osorio M. S. Statistical disclosure in two-dimensional tables: Positive tables. JASA Theory and Methods (1994) 89:1547–1557Google Scholar
• Chvátal V.Linear Programming (1980) (W.H. Freeman & Co., New York) Google Scholar
• Cox L. H. Suppression methodology and statistical disclosure control. JASA (1980) 75:377–385Crossref, Google Scholar
• Cox L. H. New results in disclosure avoidance for tabulations. Internat. Statist. Institute, Proc. 46th Session (1987) Voorburg, The Netherlands:83–84Google Scholar
• Cox L. H. Solving confidentiality protection problems in tabulations using network optimization: A network model for cell suppression in U.S. economic censuses. (1992) . Internat. Seminar on Statist. Confidentiality. Eurostat, Dublin, Ireland, 229–45Google Scholar
• Cox L. H. Network models for complementary cell suppression. JASA (1995) 90:1453–1562Crossref, Google Scholar
• Cox L. H., Fagan J. T., Greenberg B. V., Hammig R. Research at the Census Bureau into disclosure avoidance techniques for tabular data. Proc. Sect. Survey Res. Methods (1986) (American Statistical Association, Washington, D.C.) 388–393Google Scholar
• Denning D. E. Secure statistical databases with random sample queries. ACM Trans. Database Systems (1980) 5:291–315Crossref, Google Scholar
• De Vries R. E. Disclosure control of tabular data using subtables. (1993) . Report, Department of Statistical Methods, Statistics Netherlands, Voorburg, The NetherlandsGoogle Scholar
• Duncan G. T., Chapman Audrey R. Data for health: Privacy and access standards for a health care information ethics. Health Care and Information Ethics: Protecting Fundamental Human Rights (1997) (Sheed and Ward, Kansas City, MO) Google Scholar
• Duncan G. T., Fienberg S. E. Obtaining information while preserving privacy: A Markov perturbation method for tabular data. Proc. Statist. Data Protection (1998) (Lisbon, Portugal). Eurostat‘98 MarchGoogle Scholar
• Duncan G. T., Lambert D. Disclosure-limited data dissemination. JASA (1986) 81:10–28(With discussion by L. Cox, O. Frank, J. Gastwirth, H. Roberts.)Crossref, Google Scholar
• Duncan G. T., Lambert D. The risk of disclosure of microdata. J. Bus. Econom. Statist. (1989) 7:207–217Google Scholar
• Duncan G. T., Pearson R. W. Enhancing access to data while protecting confidentiality: Prospects for the future. Statist. Sci. (1991) 6:219–239Crossref, Google Scholar
• Duncan G. T., Krishnan R., Padman R., Reuther P., Roehrig S. F. Cell suppression to limit content-based disclosure. Proc. Thirtieth Ann. Hawaii Internat. Conf. System Sci. (1997) Maui, HawaiiCrossref, Google Scholar
• Ernst L. (1989) . Further applications of linear programming to sampling problems. SRD report: Census/SRD/RR-89-05 (1989), Bureau of the Census, Washington, D.C.Google Scholar
• Fellegi I. P. On the question of statistical confidentiality. JASA (1972) 67:7–18Crossref, Google Scholar
• Fienberg S. Fréchet and Bonferroni bounds for multiway tables of counts with applications to disclosure limitation. Proc. Statist. Data Protection (1998) (Eurostat, Lisbon, Portugal) . ‘98 MarchGoogle Scholar
• Fréchet M.Les Probabilités Associées a un Système d'Événments Compatibles et Dépendants (1940) (Hermann & Cie, Paris, France) . Priemiere PartieGoogle Scholar
• Gopal R., Goes P., Garfinkel R. Interval protection of confidential information in a database. INFORMS J. Comput. (1998) 10(3):309–322Link, Google Scholar
• Gusfield D. A graph theoretic approach to statistical data security. SIAM J. Comput. (1988) 17:552–571Crossref, Google Scholar
• Kelly T., Golden S., Assad A. Controlled rounding of tabular data. Oper. Res. (1990) 38:760–772Link, Google Scholar
• Kelly J., Golden S., Assad A. Cell suppression: Disclosure protection for sensitive tabular data. NETWORKS (1992) 22:397–417Crossref, Google Scholar
• Kwerel S. M., Kotz S., Johnson N. L. Fréchet bounds. Encyclopedia of Statistical Sciences (1988) (Wiley & Sons, New York) 202–209Google Scholar
• Lougee-Heimer R. Guaranteeing confidentiality: The protection of tabular data. (1989) . Masters thesis, Department of Mathematical Sciences, Clemson University, South CarolinaGoogle Scholar
• Muralidhar K., Batra D., Kirs P. Accessibility, security, and accuracy in statistical databases: The case for the multiplicative fixed data perturbation approach. Management Sci. (1995) 40:1549–1563Link, Google Scholar
• Nargundkar M. S., Saveland W. Random rounding of tables to prevent statistical disclosure. Proc. Soc. Statist. Sect., Amer. Statist. Assoc. (1972) Washington, D.C.:382–387Google Scholar
• Repsilber D. Safeguarding secrecy in aggregative data. Proc. 1991 Internat. Seminar Statist. Confidentiality (1991) (Eurostat, Dublin, Ireland) 353–368Google Scholar
• Repsilber D. Preservation of confidentiality in aggregated data. Second Internat. Seminar Statist. Confidentiality (1993) . LuxembourgGoogle Scholar
• Robertson D. A. Automated disclosure control at Statistics Canada. (1994) . Working paper, Statistics Canada, Ottawa, OntGoogle Scholar
• Rowe E. Some considerations in the use of linear networks to suppress tabular data. Proc. Sect. Survey Res. Methods (1991) (American Statistical Association, Washington D.C.) 357–362Google Scholar
• Sande G. Automated cell suppression to preserve confidentiality of business statistics. Stat. J. United Nations (1984) 33–41ECE 2 Geneva, SwitzerlandGoogle Scholar
• Schlörer J. Identification and retrieval of personal records from a statistical data bank. Methods Info. Med. (1975) 15:7–13Google Scholar
• Sullivan C., Zayatz L. A network flow disclosure avoidance system applied to the census of agriculture. Proc. Sect. Survey Res. Methods (1991) (American Statistical Association, Washington D.C.) 363–368Google Scholar
• Willenborg L., de Waal T.Statistical Disclosure Control in Practice (1996) (Springer-Verlag, New York) Crossref, Google Scholar
• Zayatz L. Linear programming methodology used for disclosure avoidance purposes at the census bureau. Proc. Sect. Survey Res. Methods (1992) (American Statistical Association, Washington, D.C.) Google Scholar