Efficient Private Learning
About the Project
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.