Efficient Private Learning

Recent advances in MPC have allowed for systems which compute aggregate statistics on inputs from many players. We have noticed, however, that such systems place a large computational burden on the client. We use share-conversion technology to compute the same aggregate statistics as the current state-of-the-art at a lower cost to the client by leveraging the beneficial properties of various secret-sharing schemes.