Private multiparty sampling and approximation of vector combinations

We consider the problem of private efficient data mining of vertically-partitioned databases. Each of several parties holds a column of a data matrix (a vector) and the parties want to investigate the componentwise combination of their vectors. The parties want to minimize communication and local computation while guaranteeing privacy in the sense that no party learns more than necessary. Sublinear-communication private protocols have primarily been studied only in the two-party case. In contrast, this work focuses on multi-party settings.