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Secure analysis of distributed chemical databases without data integration

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Summary

We present a method for performing statistically valid linear regressions on the union of distributed chemical databases that preserves confidentiality of those databases. The method employs secure multi-party computation to share local sufficient statistics necessary to compute least squares estimators of regression coefficients, error variances and other quantities of interest. We illustrate our method with an example containing four companies’ rather different databases.

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Acknowledgements

This research was supported by NSF Grant EIA-0131884 to the National Institute of Statistical Sciences (NISS) and by the HighQ Foundation. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation. The data and structures used in this paper are available at www.niss.org/PowerMV.

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Author notes

  1. Xiaodong Lin

    Present address: University of Cincinnati, Cincinnati, OH, USA

  2. Ashish P. Sanil

    Present address: Bristol-Myers Squibb, Princeton, NJ, USA

Authors and Affiliations

  1. National Institute of Statistical Sciences Research, Triangle Park, NC, 27709-4006, USA

    Alan F. Karr, Jun Feng, Xiaodong Lin, Ashish P. Sanil & S. Stanley Young

  2. Duke University, Durham, NC, 27708, USA

    Jerome P. Reiter

Authors
  1. Alan F. Karr
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  2. Jun Feng
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  3. Xiaodong Lin
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  4. Ashish P. Sanil
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  5. S. Stanley Young
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  6. Jerome P. Reiter
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Correspondence to Alan F. Karr.

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Karr, A.F., Feng, J., Lin, X. et al. Secure analysis of distributed chemical databases without data integration. J Comput Aided Mol Des 19, 739–747 (2005). https://doi.org/10.1007/s10822-005-9011-5

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  • DOI: https://doi.org/10.1007/s10822-005-9011-5

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