Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases

Published Online:https://doi.org/10.1287/mnsc.2014.2026

References

  • Aghion P, Bloom N, Blundell R, Griffith R, Howitt P (2005) Competition and innovation: An inverted U relationship. Quart. J. Econom. 120(2):701–728.Google Scholar
  • Bertino E, Byun J, Li N (2005) Privacy-Preserving Database Systems in Foundations of Security Analysis and Design III. Aldini A, Gorrieri R, Martinelli F, eds., Lecture Notes in Computer Science 3655 (Springer, Berlin), 178–206.CrossrefGoogle Scholar
  • Blattberg RC, Kim B, Neslin SA (2008) Database Marketing: Analyzing and Managing Customers (Springer, New York).CrossrefGoogle Scholar
  • Chen HY (2004) Nonparametric and semiparametric models for missing covariates in parametric regression. J. Amer. Statist. Assoc. 99(468):1176–1189.CrossrefGoogle Scholar
  • Chen HY (2007) A semiparametric odds ratio model for measuring association. Biometrics 63(2):413–421.CrossrefGoogle Scholar
  • Dalenius T, Reiss SP (1982) Data-swapping: A technique for disclosure control. J. Statist. Planning Inference 6(1):73–85.CrossrefGoogle Scholar
  • Desai P (2013) Marketing science replication and disclosure policy. Marketing Sci. 32(1):1–3.LinkGoogle Scholar
  • Genest C, Neslehova J (2007) A primer on copulas for count data. ASTIN Bull. 37(2):475–515.CrossrefGoogle Scholar
  • Goldfarb A, Tucker C (2012) Privacy and Innovation, Lerner J, Stern S, eds. Innovation Policy and the Economy, Vol. 12 (National Bureau of Economic Research, Cambridge, MA), 65–89.CrossrefGoogle Scholar
  • Kalvenes J, Basu A (2006) Design of robust business-to-business electronic marketplaces with guaranteed privacy. Management Sci. 52(11):1721–1736.LinkGoogle Scholar
  • Kim G, Silvapulle MJ, Silvapulle P (2007) Comparison of semiparametric and parametric methods for estimating copulas. Comput. Statist. Data Anal. 51(6):2836–2850.CrossrefGoogle Scholar
  • Lee S, Genton MG, Arellano-Valle RB (2010) Perturbation of numerical confidential data via skew-t distributions. Management Sci. 56(2):318–333.LinkGoogle Scholar
  • Li X, Sarkar S (2011) Protecting privacy against record linkage disclosure: A bounded swapping approach for numeric data. Inform. Systems Res. 22(4):774–789.LinkGoogle Scholar
  • McCullagh P, Nelder JA (1989) Generalized Linear Models, 2nd ed. (Chapman and Hall/CRC, Boca Raton, FL).CrossrefGoogle Scholar
  • Mela C (2011) Data selection and procurement. Marketing Sci. 30(6):965–976.LinkGoogle Scholar
  • Menon S, Sarkar S (2007) Minimizing information loss and preserving privacy. Management Sci. 53(1):101–116.LinkGoogle Scholar
  • Miller A, Tucker C (2011) Encryption and the loss of patient data. J. Policy Anal. Management 30(3):534–556.CrossrefGoogle Scholar
  • Mokyr J (2013) Technopessimism is bunk. PBS (July 26), http://www.pbs.org/newshour/rundown/technopessimism-is-bunk/.Google Scholar
  • Muralidhar K, Sarathy R (2003) A theoretical basis for perturbation methods. Statist. Comput. 13(4):329–335.CrossrefGoogle Scholar
  • Muralidhar K, Sarathy R (2006) Data shuffling–A new masking approach for numerical data. Management Sci. 52(5):658–670.LinkGoogle Scholar
  • Muralidhar K, Batra D, Kirs PJ (1995) Accessibility, security, and accuracy in statistical database: The case for the multiplicative fixed data perturbation approach. Management Sci. 41(9):1549–1564.LinkGoogle Scholar
  • Muralidhar K, Parsa R, Sarathy R (1999) A general additive data perturbation method for database security. Management Sci. 45(10):1399–1415.LinkGoogle Scholar
  • Muralidhar K, Sarathy R, Parsa R (2001) An improved security requirement for data perturbation with implications for E-commerce. Decision Sci. 32(4):683–698.CrossrefGoogle Scholar
  • Nocedal J, Wright SJ (1999) Large-scale quasi-Newton and partially separable optimization. Numerical Optimization, Springer Series in Operations Research and Financial Engineering (Springer-Verlag, New York), 222–249.CrossrefGoogle Scholar
  • Qian Y (2007) Do national patent laws stimulate domestic innovation in a global patenting environment? A cross-country analysis of pharmaceutical patent protection, 1978–2002. Rev. Econom. Statist. 89(3):436–453.CrossrefGoogle Scholar
  • Qian Y, Xie H (2011) No customer left behind: A distribution-free Bayesian approach to account for missing xs in marketing models. Marketing Sci. 30(4):717–736.LinkGoogle Scholar
  • Qian Y, Xie H (2014) Which brand purchasers are lost to counterfeits? An application of new data fusion approaches. Marketing Sci. 33(3):437–448.LinkGoogle Scholar
  • Qu L, Qian Y, Xie H (2009) Copula density estimation by total variation penalized likelihood. Comm. Statist. Simulation Comput. 38(9):1891–1908.CrossrefGoogle Scholar
  • Raghunathan TE, Reiter JP, Rubin DB (2003) Multiple imputation for statistical disclosure limitation. J. Official Statist. 19(1):1–16.Google Scholar
  • Reiter JP (2005) Releasing multiply-imputed, synthetic public use microdata: An illustration and empirical study. J. Royal Statist. Soc. Ser. A 168:185–205.CrossrefGoogle Scholar
  • Reiter JP, Raghunathan TE (2007) The multiple adaptations of multiple imputation. J. Amer. Statist. Assoc. 102(480):1462–1471.CrossrefGoogle Scholar
  • Rubin DB (1993) Discussion: Statistical disclosure limitation. J. Official Statist. 9(2):461–468.Google Scholar
  • Sarathy R, Muralidhar K, Parsa R (2002) Perturbing nonnormal confidential attributes: The copula approach. Management Sci. 48(12):1613–1627.LinkGoogle Scholar
  • Scheuer EM, Stoller DS (1962) On the generation of normal random vectors. Technometrics 4(2):278–281.CrossrefGoogle Scholar
  • Simpson GR (2001) Bureau blurs data to keep names confidential. Wall Street Journal (February 14):B1–B2.Google Scholar
  • Willenborg L, de Waal T (2001) Elements of Statistical Disclosure Control (Springer, New York).CrossrefGoogle Scholar
  • Winer RS (2001) A framework for customer relationship management. California Management Rev. 43(4):89–105.CrossrefGoogle Scholar
  • Xiao X, Tao Y (2006) Personalized privacy preservation. Chaudhuri S, Hristidis V, Polyzotis N, eds. Proc. ACM SIGMOD Internat. Conf. Management of Data (Association for Computing Machinery, New York), 229–240.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.