  Consider two data providers that want to contribute data to a certain
learning model. Recent works have shown that the value of the data of one of
the providers is dependent on the similarity with the data owned by the other
provider. It would thus be beneficial if the two providers can calculate the
similarity of their data, while keeping the actual data private. In this work,
we devise multiparty computation-protocols to compute similarity of two data
sets based on correlation, while offering controllable privacy guarantees. We
consider a simple model with two participating providers and develop methods to
compute exact and approximate correlation, respectively, with controlled
information leakage. Both protocols have computational and communication
complexities that are linear in the number of data samples. We also provide
general bounds on the maximal error in the approximation case, and analyse the
resulting errors for practical parameter choices.
