Karr Alan F, Feng Jun, Lin Xiaodong, Sanil Ashish P, Young S Stanley, Reiter Jerome P
National Institute of Statistical Sciences Research, Triangle Park, NC 27709-4006, USA.
J Comput Aided Mol Des. 2005 Sep-Oct;19(9-10):739-47. doi: 10.1007/s10822-005-9011-5. Epub 2005 Nov 3.
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.
我们提出了一种方法，可对分布式化学数据库的联合进行具有统计有效性的线性回归，同时保护这些数据库的机密性。该方法采用安全多方计算来共享计算回归系数、误差方差和其他感兴趣量的最小二乘估计所需的局部充分统计量。我们用一个包含四家公司截然不同的数据库的示例来说明我们的方法。
