Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns

Published Online:https://doi.org/10.1287/isre.1050.0056

References

  • Agrawal R., Srikant R. Fast algorithms for mining association rules. Proc. 20th Internat. Conf. Very Large Databases (1994) (Morgan Kaufmann Publishers, San Francisco, CA) 487–499Google Scholar
  • Atallah M., Bertino E., Elmagarmid A., Ibrahim M., Verykios V. Disclosure limitation of sensitive rules. Proc. 1999 Workshop Knowledge Data Engrg. Exchange, (KDEX ’99) (1999) (IEEE Computer Society, Washington, D.C.) 45–52Google Scholar
  • Bayardo R. Efficiently mining long patterns from databases. Proc. ACM-SIGMOD Internat. Conf. Management Data (1998) (ACM Press, New York) CrossrefGoogle Scholar
  • Bodon F. A fast APRIORI implementation. Proc. Workshop Frequent Itemset Mining Implementations (FIMI’03) (2003) 90CEUR-WS.org, CEUR Workshop ProceedingsGoogle Scholar
  • Brijs T., Swinnen G., Vanhoof K., Wets G. The use of association rules for product assortment decisions: A case study. Proc. 5th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (1999) (ACM Press, New York) CrossrefGoogle Scholar
  • Choudhury S., Duncan G., Krishnan R., Roehrig S., Mukherjee S. Disclosure detection in multivariate categorical databases: Auditing confidentiality protection through two new matrix operators. Management Sci. (1999) 45(12):1710–1723LinkGoogle Scholar
  • Clifton C., Marks D. Security and privacy implications of data mining. Proc. ACM-SIGMOD Workshop Data Mining Knowledge Discovery (1996) (Dept. of Computer Science, University of British Columbia, Montreal, Canada) 15–19Google Scholar
  • Dasseni E., Verykios V., Elmagarmid A., Bertino E. Hiding association rules by using confidence and support. Proc. 4th Internat. Inform. Hiding Workshop (IHW) (2001) (Springer-Verlag, London, UK) 369–383CrossrefGoogle Scholar
  • Du W., Zhan Z. Building decision tree classifier on private data. Proc. IEEE ICDM Workshop Privacy, Security Data Mining (2002) (Australian Computer Society, Darlinghurst, Australia) 1–8Google Scholar
  • Evfimievski A., Srikant R., Agrawal R., Gehrke J. Privacy preserving mining of association rules. Inform. Systems (2004) 29:343–364CrossrefGoogle Scholar
  • Garey M., Johnson D.Computers and Intractability: A Guide to the Theory of NP-Completeness (1979) (W. H. Freeman and Company, New York) Google Scholar
  • Garfinkel R., Gopal R., Goes P. Privacy protection of binary confidential data against deterministic, stochastic, and insider threat. Management Sci. (2002) 48(6):749–764LinkGoogle Scholar
  • Geurts K., Wets G., Brijs T., Vanhoof K. Profiling high frequency accident locations using association rules. Proc. 82nd Annual Meeting Transportation Res. Board (2003) 18CrossrefGoogle Scholar
  • Goethals B. Efficient frequent pattern mining. (2002) . Ph.D. thesis, Universitiet Limburg, Diepenbeek, BelgiumGoogle Scholar
  • Goethals B., Zaki M. Advances in frequent itemset mining implementations: Introduction to FIMI’03. Proc. Workshop Frequent Itemset Mining Implementations (FIMI’03) (2003) November(Melbourne, FL)Google 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(3):501–516LinkGoogle Scholar
  • ILOG.ILOG CPLEX 9.0 User’s Manual (2003) (ILOG Inc., Gentilly, France) Google Scholar
  • Oliveira S., Zaïane O. Privacy preserving frequent itemset mining. Proc. IEEE ICDM Workshop Privacy, Security Data Mining (2002) (Australian Computer Society, Darlinghurst, Australia) 43–54Google Scholar
  • Oliveira S., Zaïane O. Protecting sensitive knowledge by data sanitization. Proc. 3rd IEEE Internat. Conf. Data Mining (ICDM’03) (2003) (IEEE Computer Society, Washington, D.C.) 99–106CrossrefGoogle Scholar
  • Piatetsky-Shapiro G. Knowledge discovery in personal data vs. privacy: A minisymposium. IEEE Expert (1995) 10(2):46–47Google Scholar
  • Reddy M., Wang R. Estimating data accuracy in a federated database environment. Proc. 6th Internat. Conf. Inform. Systems Management Data (CISMOD) (1995) (Springer-Verlag, Secaucus, NJ) 115–134CrossrefGoogle Scholar
  • Sarathy R., Muralidhar K. The security of confidential numerical data in databases. Inform. Systems Res. (2002) 13(4):389–403LinkGoogle Scholar
  • Sarathy R., Muralidhar K., Parsa R. Perturbing nonnormal confidential attributes: The copula approach. Management Sci. (2002) 48(12):1613–1627LinkGoogle Scholar
  • Saygin Y., Verykios V., Clifton C. Using unknowns to prevent discovery of association rules. SIGMOD Rec. (2001) 30(4CrossrefGoogle Scholar
  • Verykios V., Elmagarmid A., Bertino E., Saygin Y., Dasseni E. Association rule hiding. IEEE Trans. Knowledge Data Engrg. (2004a) 16(4):434–447CrossrefGoogle Scholar
  • Verykios V., Bertino E., Fovino I., Provenza L., Saygin Y., Theodoridis Y. State-of-the-art in privacy preserving data mining. SIGMOD Rec. (2004b) 33(1):50–57CrossrefGoogle Scholar
  • Zheng Z., Kohavi R., Mason L. Real world performance of association rule algorithms. Proc. 7th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (1999) (ACM Press, New York) 401–406Google Scholar
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