Abstract: Methods and systems are provided for evaluating Machine Learning models in a Machine-Learning-As-A-Service context, whereby the secrecy of the parameters of the Machine Learning models and the privacy of the input data fed to the Machine Learning model are preserved as much as possible, while requiring the exchange between a client and an MLaaS server of as few messages as possible. The provided methods and systems are based on the use of additive homomorphic encryption in the context of Machine Learning models that are equivalent to models that are based on the evaluation of an inner product of on the one hand a vector that is a function of extracted client data and on the other hand a vector of model parameters. In some embodiments the client computes an inner product of extracted client data and a vector of model parameters that are encrypted with an additive homomorphic encryption algorithm.