Abstract: A multi-party privacy-preserving machine learning system is described which has a trusted execution environment comprising at least one protected memory region. An code loader at the system loads machine learning code, received from at least one of the parties, into the protected memory region. A data uploader uploads confidential data, received from at least one of the parties, to the protected memory region. The trusted execution environment executes the machine learning code using at least one data-oblivious procedure to process the confidential data and returns the result to at least one of the parties, where a data-oblivious procedure is a process where any patterns of memory accesses, patterns of disk accesses and patterns of network accesses are such that the confidential data cannot be predicted from the patterns.