  In this work, we study how to securely evaluate the value of trading data
without requiring a trusted third party. We focus on the important machine
learning task of classification. This leads us to propose a provably secure
four-round protocol that computes the value of the data to be traded without
revealing the data to the potential acquirer. The theoretical results
demonstrate a number of important properties of the proposed protocol. In
particular, we prove the security of the proposed protocol in the
honest-but-curious adversary model.
