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Secure Statistical Analysis of Distributed Databases

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Statistical Methods in Counterterrorism

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References

  1. Beaton, A. E. 1964. “The use of special matrix operations in statistical calculus.” Educational Testing Service Research Bulletin, vol. RB-64-51.

    Google Scholar 

  2. Benaloh, J. 1987. “Secret sharing homomorphisms: Keeping shares of a secret sharing.” In Advances in cryptology: Proceedings of CRYPTO’ 86, edited by G. Goos and J. Hartmanis, Volume 263 of Lecture Notes in Computer Science, 251–260. New York: Springer-Verlag.

    Google Scholar 

  3. Bishop, Y. M. M., S. E. Fienberg, and P. W. Holland. 1975. Discrete multivariate analysis: Theory and practice. Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  4. Brent, R. P. 1973. Algorithms for minimization without derivatives. Englewood Cliffs, NJ: Prentice-Hall.

    MATH  Google Scholar 

  5. Defays, D., and N. Anwar. 1995. “Micro-aggregation: A generic method.” Proceedings of the 2nd International Symposium on Statistical Confidentiality. Luxembourg: Office for Official Publications of the European Community, 69–78.

    Google Scholar 

  6. Defays, D., and P. Nanopoulos. 1993. “Panels of enterprises and confidentiality: The small aggregates method.” Proceedings of the 92 Symposium on Design and Analysis of Longitudinal Surveys. Ottawa: Statistics Canada, 195–204.

    Google Scholar 

  7. Dobra, A., S. E. Fienberg, A. F. Karr, and A. P. Sanil. 2002. “Software systems for tabular data releases.” International Journal of Uncertainty, Fuzziness, and Knowledge Based Systems 10(5): 529–544.

    Article  Google Scholar 

  8. Dobra, A., A. F. Karr, and A. P. Sanil. 2003. “Preserving confidentiality of high-dimensional tabular data: Statistical and computational issues.” Statistics and Computing 13(4): 363–370.

    Article  MathSciNet  Google Scholar 

  9. Doyle, P., J. Lane, J. J. M. Theeuwes, and L. V. Zayatz. 2001. Confidentiality, disclosure, and data access: Theory and practical application for statistical agencies. Amsterdam: Elsevier.

    Google Scholar 

  10. Du, W., Y. Han, and S. Chen. 2004. “Privacy-preserving multivariate statistical analysis: Linear regression and classification.” Proceedings 4th SIAM International Conference on Data Mining. 222–233.

    Google Scholar 

  11. Du, W., and Z. Zhan. 2002. “A practical approach to solve secure multiparty computation problems.” New Security Paradigms Workshop 2002. New York: ACM Press, 127–135.

    Chapter  Google Scholar 

  12. Duncan, G. T., T. B. Jabine, and V. A. de Wolf. 1993. Private lives and public policies: Confidentiality and accessibility of government statistics. Washington, DC: National Academies Press. Panel on Confidentiality and Data Access.

    Google Scholar 

  13. Duncan, G. T., and S. A. Keller-McNulty. 2001. “Mask or impute?” Proceedings of ISBA 2000.

    Google Scholar 

  14. Duncan, G. T., S. A. Keller-McNulty, and S. L. Stokes. 2004. “Disclosure risk vs. data utility: The R-U confidentiality map.” Under revision for Management Science.

    Google Scholar 

  15. Duncan, G. T., and L. Stokes. 2004. “Disclosure risk vs. data utility: The R-U confidentiality map as applied to topcoding.” Chance 17(3):16–20.

    MathSciNet  Google Scholar 

  16. Federal Committee on Statistical Methodology. 1994. Report on statistical disclosure limitation methodology. Washington, DC: U. S. Office of Management and Budget.

    Google Scholar 

  17. Fienberg, S. E., and L. C. R. J. Willenborg, eds. 1998. Special issue on disclosure limitation methods for protecting the confidentiality of statistical data. Volume 14(4). Journal of Official Statistics.

    Google Scholar 

  18. Goldreich, O., S. Micali, and A. Wigderson. 1987. “How to play any mental game.” Proceedings of the 19th Annual ACM Symposium on Theory of Computing. 218–229.

    Google Scholar 

  19. Goldwasser, S. 1997. “Multi-party computations: Past and present.” Proceedings 16th Annual ACM Symposium on Principles of Distributed Computing. New York: ACM Press, 1–6.

    Google Scholar 

  20. Gomatam, S., A. F. Karr, J. P. Reiter, and A. P. Sanil. 2005. “Data dissemination and disclosure limitation in a world without microdata: A risk-utility framework for remote access analysis servers.” Statistical Sciences 20(2): 163–177. http://www.niss.org/dgii/technicalreports.html.

    Article  MathSciNet  Google Scholar 

  21. Gomatam, S., A. F. Karr, and A. P. Sanil. January 2004. “Data swapping as a decision problem.” Journal of Official Statistics, (Revised October 2004), http://www.niss.org/dgii/technicalreports.html.

    Google Scholar 

  22. Harrison, D., and D. L. Rubinfeld. 1978. “Hedonic prices and the demand for clean air.” Journal Environmental Economics Management 5:81–102.

    Article  Google Scholar 

  23. Hastie, T., R. Tibshirani, and J. Friedman. 2001. The elements of statistical learning: Data mining, inference, and prediction. New York: Springer-Verlag.

    MATH  Google Scholar 

  24. Hoerl, A. E., and R. Kennard. 1970. “Ridge regression: Biased estimation for nonorthogonal problems.” Technometrics 12:55–67.

    Article  Google Scholar 

  25. Karr, A. F., J. Feng, X. Lin, J. P. Reiter, A. P. Sanil, and S. S. Young. 2005. “Secure analysis of distributed chemical databases without data integration.” Journal Computer-Aided Molecular Design, pp. 1–9. http://www.niss.org/dgii/technicalreports.html.

    Google Scholar 

  26. Karr, A. F., C. N. Kohnen, A. Oganian, J. P. Reiter, and A. P. Sanil. June 2005. “A framework for evaluating the utility of data altered to protect confidentiality.” The American Statistician, http://www.niss.org/dgii/technicalreports.html.

    Google Scholar 

  27. Karr, A. F., J. Lee, A. P. Sanil, J. Hernandez, S. Karimi, and K. Litwin. 2001. “Disseminating information but protecting confidentiality.” IEEE Computer 34(2): 36–37.

    Google Scholar 

  28. Karr, A. F., X. Lin, J. P. Reiter, and A. P. Sanil. 2004. “Analysis of integrated data without data integration.” Chance 17(3): 26–29.

    MathSciNet  Google Scholar 

  29. Karr, A. F., X. Lin, J. P. Reiter, and A. P. Sanil. 2004. “Privacy preserving analysis of vertically partitioned data using se cure matrix products.” Submitted to Journal of Official Statistics, http://www.niss.org/dgii/technicalreports.html.

    Google Scholar 

  30. Karr, A. F., X. Lin, J. P. Reiter, and A. P. Sanil. 2005. “Secure regression on distributed databases.” Journal of Computational and Graphical Statistics 14(2): 263–279.

    Article  MathSciNet  Google Scholar 

  31. Karr, A. F., A. P. Sanil, and D. L. Banks. 2006. “Data quality: A statistical perspective.” Statistical Methodology, 3(2):137–173 http://www.niss.org/dgii/technicalreports.html.

    Article  MathSciNet  Google Scholar 

  32. Karr, A. F., A. P. Sanil, J. Sacks, and E. Elmagarmid. 2001. “Affiliates workshop on data quality.” Workshop report, National Institute of Statistical Sciences. http://www.niss.org/affiliates/dqworkshop/report/dq-report.pdf.

    Google Scholar 

  33. Lee, J., C. Holloman, A. F. Karr, and A. P. Sanil. 2001. “Analysis of aggregated data in survey sampling with application to fertilizer/pesticide usage surveys.” Research in Official Statistics 4:101–116.

    Google Scholar 

  34. Powell, M. J. D. 1964. “An efficient method for finding the minimum of a function of several variables without calculating derivatives.” Computer Journal 7:152–162.

    Article  MathSciNet  Google Scholar 

  35. Raghunathan, T. E., J. P. Reiter, and D. B. Rubin. 2003. “Multiple imputation for statistical disclosure limitation.” Journal of Official Statistics 19:1–16.

    Google Scholar 

  36. Reiter, J. P. 2003. “Inference for partially synthetic, public use microdata sets.” Survey Methodology 29:181–188.

    Google Scholar 

  37. Reiter, J. P. 2003. “Model diagnostics for remote access regression servers.” Statistics and Computing 13:371–380.

    Article  MathSciNet  Google Scholar 

  38. Reiter, J. P., A. F. Karr, C. N. Kohnen, X. Lin, and A. P. Sanil. 2004. “Secure regression for vertically partitioned, partially overlapping data.” Proceedings of the Joint Statistical Meetings. American Statistical Association.

    Google Scholar 

  39. Sanil, A. P., A. F. Karr, X. Lin, and J. P. Reiter. 2004. “Privacy preserving regression modelling via distributed computation.” Proceedings Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 677–682.

    Chapter  Google Scholar 

  40. Schafer, J. L. 2003. Analysis of incomplete multivariate data. London: Chapman & Hall.

    Google Scholar 

  41. Sweeney, L. 1997. “Computational disclosure control for medical microdata: The Datafly system.” Record Linkage Techniques 1997: Proceedings of an International Workshop and Exposition. 442–453.

    Google Scholar 

  42. Trottini, M. 2003. “Decision models for data disclosure limitation.” Ph.D. diss., Carnegie Mellon University. http://www.niss.org/dgii/TR/Thesis-Trottini-final.pdf.

    Google Scholar 

  43. Willenborg, L. C. R. J., and T. de Waal. 1996. Statistical disclosure control in practice. New York: Springer-Verlag.

    MATH  Google Scholar 

  44. Willenborg, L. C. R. J., and T. de Waal. 2001. Elements of statistical disclosure control. New York: Springer-Verlag.

    MATH  Google Scholar 

  45. Yao, A. C. 1982. “Protocols for secure computations.” Proceedings of the 23rd Annual IEEE Symposium on Foundations of Computer Science. New York: ACM Press, 160–164.

    Google Scholar 

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Author information

Authors and Affiliations

  1. National Institute of Statistical Sciences, USA

    Alan F. Karr

  2. Statistical and Applied Mathematical Sciences Institute and Department of Mathematical Sciences, University of Cincinnati, USA

    Xiaodong Lin

  3. National Institute of Statistical Sciences and Bristol-Myers Squibb, USA

    Ashish P. Sanil

  4. Institute of Statistics and Decision Sciences, Duke University, USA

    Jerome P. Reiter

Authors
  1. Alan F. Karr
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  2. Xiaodong Lin
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  3. Ashish P. Sanil
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  4. Jerome P. Reiter
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Editor information

Editors and Affiliations

  1. Los Alamos National Laboratory, Los Alamos, NM, 87545, USA

    Alyson G. Wilson  & Gregory D. Wilson  & 

  2. Department of Systems Engineering, Naval Postgraduate School, Monterey, CA, 93943, USA

    David H. Olwell

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Karr, A.F., Lin, X., Sanil, A.P., Reiter, J.P. (2006). Secure Statistical Analysis of Distributed Databases. In: Wilson, A.G., Wilson, G.D., Olwell, D.H. (eds) Statistical Methods in Counterterrorism. Springer, New York, NY. https://doi.org/10.1007/0-387-35209-0_14

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