SEARCH

SEARCH BY CITATION

References

  • [1]
    G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, and A. Zhu, “Anonymizing Tables,” Proc. 10th Internat. Conf. on Database Theory (ICDT '05) (Edinburgh, UK, 2005), LNCS vol. 3363, pp. 246258.
  • [2]
    M. Andrews, J. Cao, and J. McGowan, “Measuring Human Satisfaction in Data Networks,” Proc. 25th IEEE Internat. Conf. on Comput. Commun. (INFOCOM '06) (Oakland, CA, 2006).
  • [3]
    M. Barbaro and T. Zeller, “A Face Is Exposed for AOL Searcher No. 4417749,” New York Times, Aug. 9, 2006, p.A1, <http://www.nytimes.com/2006/08/09/technology/09aol.html?pagewanted=all&_r=0>.
  • [4]
    R. J. Bayardo and R. Agrawal, “Data Privacy Through Optimal k-Anonymization,” Proc. 21st Internat. Conf. on Data Eng. (ICDE '05) (Tokyo, Jpn., 2005), pp. 217228.
  • [5]
    R. Bhaskar, S. Laxman, A. Smith, and A. Thakurta, “Discovering Frequent Patterns in Sensitive Data,” Proc. 16th ACM SIGKDD Internat. Conf. on Knowl. Discov. and Data Mining (KDD '10) (Washington, DC, 2010), pp. 503512.
  • [6]
    J.-W. Byun, A. Kamra, E. Bertino, and N. Li, “Efficient k-Anonymization Using Clustering Techniques,” Proc. 12th Internat. Conf. on Database Syst. for Adv. Applic. (DASFAA '07) (Bangkok, Tha., 2007), LNCS vol. 4443, pp. 188200.
  • [7]
    C. Dwork, “Differential Privacy,” Proc. 33rd Internat. Colloquium on Automata, Languages and Programming (ICALP '06) (Venice, Ita., 2006), LNCS vol. 4052, pp. 112.
  • [8]
    D. Feldman, A. Fiat, H. Kaplan, and K. Nissim, “Private Coresets,” Proc. 41st ACM Symp. on Theory of Comput. (STOC '09) (Bethesda, MD, 2009), pp. 361370.
  • [9]
    A. E. Gelfand and A. F. M. Smith, “Sampling-Based Approaches to Calculating Marginal Densities,” J. Amer. Statist. Assoc., 85:410 (1990), 398409.
  • [10]
    N. Glady, B. Baesens, and C. Croux, “Modeling Churn Using Customer Lifetime Value,” Eur. J. Oper. Res., 197:1 (2009), 402411.
  • [11]
    J. Hadden, A. Tiwari, R. Roy, and D. Ruta, “Churn Prediction Using Complaints Data,” Internat. J. World Acad. Sci., Eng., Technol., 19 (2008), 809814.
  • [12]
    G. Jagannathan, K. Pillaipakkamnatt, and R. N. Wright, “A Practical Differentially Private Random Decision Tree Classifier,” Proc. IEEE Internat. Conf. on Data Mining Workshops (ICDMW '09) (Miami, FL, 2009), pp. 114121.
  • [13]
    D. R. Jeske, T. P. Callanan, and L. Guo, “Identification of Key Drivers of Net Promoter Score Using a Statistical Classification Model,” Efficient Decision Support Systems—Practice and Challenges from Current to Future (C. Jao, ed.), InTech, Rijeka, Cro., New York, 2011, Chapter 8.
  • [14]
    F. McSherry and R. Mahajan, “Differentially-Private Network Trace Analysis,” Proc. ACM SIGCOMM Conf. on Data Commun. (SIGCOMM '10) (New Delhi, Ind., 2010), pp. 123134.
  • [15]
    A. Meyerson and R. Williams, “On the Complexity of Optimal k-Anonymity,” Proc. 23rd ACM SIGMOD-SIGACT-SIGART Symp. on Principles of Database Syst. (PODS '04) (Paris, Fra., 2004), pp. 223228.
  • [16]
    M. C. Mozer, R. Wolniewicz, D. B. Grimes, E. Johnson, and H. Kaushansky, “Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry,” IEEE Trans. Neural Networks, 11:3 (2000), 690696.
  • [17]
    A. Narayanan and V. Shmatikov, “Robust De-Anonymization of Large Sparse Datasets,” IEEE Symp. on Security and Privacy (SP '08) (Oakland, CA, 2008), pp. 111125.
  • [18]
    F. Reichheld, The Ultimate Question: Driving Good Profits and True Growth, Harvard Business School Press, Boston, MA, 2006.
  • [19]
    Y. Richter, E. Yom-Tov, and N. Slonim, “Predicting Customer Churn in Mobile Networks Through Analysis of Social Groups,” Proc. SIAM Internat. Conf. on Data Mining (SDM '10) (Columbus, OH, 2010), pp. 732741.
  • [20]
    S. Rosset, E. Neumann, U. Eick, and N. Vatnik, “Customer Lifetime Value Models for Decision Support,” Data Min. Knowl. Discov., 7:3 (2003), 321339.
  • [21]
    L. Sweeney, “k-Anonymity: A Model for Protecting Privacy,” Internat. J. Uncertain. Fuzziness Knowledge-Based Syst., 10:5 (2002), 557570.