  Preserving the privacy of individual databases when carrying out statistical
calculations has a long history in statistics and had been the focus of much
recent attention in machine learning In this paper, we present a protocol for
computing logistic regression when the data are held by separate parties
without actually combining information sources by exploiting results from the
literature on multi-party secure computation. We provide only the final result
of the calculation compared with other methods that share intermediate values
and thus present an opportunity for compromise of values in the combined
database. Our paper has two themes: (1) the development of a secure protocol
for computing the logistic parameters, and a demonstration of its performances
in practice, and (2) and amended protocol that speeds up the computation of the
logistic function. We illustrate the nature of the calculations and their
accuracy using an extract of data from the Current Population Survey divided
between two parties.
