Abstract: Privacy-preserving is becoming an increasingly important task in the Web-enabled world. Specifically we propose a novel two-party privacy-preserving classification solution called collaborative classification mechanism for Privacy-preserving(C\+2MP\+2) that is inspired from mean value and covariance matrix globally stating data location and direction, and the fact that sharing those global information with others will not disclose ones own privacy. This model collaboratively trains the decision boundary from two hyper-planes individually constructed by ones own privacy information and counter-partys global information. As a major contribution, we show that C\+2MP\+2 can protect both data-entries and number of entries. We describe the C\+2MP\+2 model definition, provide the geometrical interpretation, and present theoretical justifications. To guarantee the security of testing procedure, we then develop a testing algorithm based on homomorphic encryption scheme. Moreover, we show that C\+2MP\+2 can be transformed into existing minimax probability machine (MPM), support vector machine (SVM) and maxi-min margin machine (M\+4) model when privacy data satisfies certain conditions. We also extend C\+2MP\+2 to a nonlinear classifier by exploiting kernel trick without privacy disclosure. Furthermore, we perform a series of evaluations on both toy data sets and real-world benchmark data sets. Comparison with MPM and SVM demonstrates the advantages of our new model in protecting privacy.