Two-party privacy preserving decision tree evaluation using additively homomorphic encryption

Abstract

Disclosed herein are system, method, and computer program product embodiments for evaluating a decision tree in a privacy preserving manner. In embodiments, s first server receives a first partial share of the decision tree and encrypted input data from a client, wherein the input data comprises a set of attributes. A second server receives a second partial share of the decision tree. Each of the first server and the second server communicate with the other server to compute a classification result over the encrypted input data using the respective partial share of the decision tree received thereby and secure multi-party computation methods, including additive homomorphic encryption and oblivious transfer, such that the classification result is computed without decrypting the encrypted input data, and such that the classification result is ultimately received by the first server. The first server then transmits the classification result to the client.