Minimizing Information Loss and Preserving Privacy

References

• Agrawal R., Srikant R. Fast algorithms for mining association rules in large databases. Proc. 20th Internat. Conf. Very Large Databases (1994) (Morgan Kaufmann, 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, 1999 (KDEX ’99) (1999) IEEE Computer Society, Washington, D.C.:45–52Google Scholar
• Atamtürk A., Savelsbergh M. Integer programming software systems. Ann. Oper. Res. (2005) 140:67–124Crossref, Google Scholar
• Bradley S., Hax A., Magnanti T.Applied Mathematical Programming (1977) (Addison-Wesley, Reading, MA) Google Scholar
• Brijs T., Swinnen G., Vanhoof K., Wets G. The use of association rules for product assortment decisions: A case study. Proc. Fifth ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (1999) (ACM Press)254–260Crossref, Google 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):1710–1723Link, Google Scholar
• Deshpande M., Karypis G. Using conjunction of attribute values for classification. CIKM ’02: Proc. Eleventh Internat. Conf. Inform. Knowledge Management, McLean, VA (2002) (ACM Press, New York) 356–364Crossref, Google Scholar
• Duncan G., Mukherjee S. Optimal disclosure limitation strategy in statistical databases: Deterring tracker attacks through additive noise. J. Amer. Statist. Assoc. (2000) 95(451):720–729Crossref, Google Scholar
• Fischetti M., Polo C., Scantamburlo M. A local branching heuristic for mixed-integer programs with 2-level variables, with an application to a telecommunication network design problem. Networks (2004) 44(2):61–72Crossref, 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):749–764Link, Google Scholar
• Goethals B. Efficient frequent pattern mining. (2002) . Ph.D. thesis, Universiteit Limburg, Diepenbeek, BelgiumGoogle Scholar
• Grean M., Shaw M. Supply-chain integration through information sharing: Channel partnership between Wal-Mart and Procter & Gamble. (2005) . IT Management Case, Center for IT and e-Business Management, University of Illinois at Urbana-Champaign, Champaign, ILGoogle Scholar
• Gruman G. Supply on demand: Manufacturers need to know what’s selling before they can produce and deliver their wares in the right quantities. (2005) . InfoWorld.com. http://www.infoworld.com/article/05/04/18/16FEsupplychain_2.htmlGoogle Scholar
• Heun C. When to share: Wal-Mart and other companies reassess their data-sharing strategies. InformationWeek.com (2001) . http://www.informationweek.com/838/data.htmGoogle Scholar
• ILOGILOG CPLEX 9.0 User’s Manual (2003) . ILOG Inc., Gentilly, FranceGoogle Scholar
• Li W., Han J., Pei J. CMAR: Accurate and efficient classification based on multiple class-association rules. ICDM ’01: Proc. First IEEE Internat. Conf. Data Mining (ICDM ’01) (2001) IEEE Computer Society, Washington, D.C.:369–376Google Scholar
• McDougall P. Collaborative business: Companies that dare to share information are cashing in on new opportunities. InformationWeek.com (2001) . http://informationweek.com/836/collaborate.htmGoogle 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
• Oliveira S., Zaïane O. Privacy preserving frequent itemset mining. Proc. IEEE ICDM Workshop Privacy, Security Data Mining (2002) Australian Computer Society, Sydney, Australia:43–54Google Scholar
• Sarathy R., Muralidhar K. The security of confidential numerical data in databases. Inform. Systems Res. (2002) 13(4):389–403Link, Google Scholar
• Verykios V., Elmagarmid A., Bertino E., Saygin Y., Dasseni E. Association rule hiding. IEEE Trans. Knowledge Data Engrg. (2004) 16(4):434–447Crossref, Google Scholar
• Yang Y., Padmanabhan B. GHIC: A hierarchical pattern-based clustering algorithm for grouping web transactions. IEEE Trans. Knowledge Data Engrg. (2005) 17(9):1300–1304Crossref, Google Scholar