Classification algorithms are widely used in the areas of machine learning and data mining.It is an important task to evaluate the classification algorithms securely when both the classification algorithm and the input data of the algorithm are private.In order to improve the efficiency of existing secure evaluation protocols

a solution based on both the algebraic decision diagram and the linear multi-branching program was presented.Firstly

a secure function evaluation protocol based on algebraic decision diagram was designed for evaluating decision functions securely.Secondly

a structure named linear multi-branching program was proposed to represent the classification algorithms.Based on both the secure function evaluation protocol and the structure of linear multi-branching program

a protocol for securely evaluating the private linear multi-branching programs was constructed.Both the correctness and the security of the new protocol were analyzed.Experimental results show that the new protocol is more efficient than the existing solutions.