Secure classifying of data with Gaussian distributions

摘要

A first party has a data vector x and a second party has a classifier defined as a set of multivariate Gaussian distributions. A secure inner dot product procedure is applied to each multivariate Gaussian distribution and the data vector x to produce a vector a_i for the first party and a vector b_i for the second party for each application. The secure inner dot product is then applied to each vector b_i and the data vector x to produce a scalar r_i for the first party and a scalar q_i for the second party for each application. A summed vector of elements [(a₁x+q₁+r₁), . . . , (a_Nx+q_N+r_N)] is formed, and an index I of the summed vector for a particular element having a maximum value is the class of the data vector x.