Secure multi-party computation methods and apparatuses for privacy protection, and computer devices

Abstract

Embodiments of this specification disclose secure multi-party computation for privacy protection. In an implementation, a method includes obtaining a fragment of first gradient data and a fragment of noise data, where the first gradient data is gradient data of a loss function. Based on the fragment of first gradient data by performing secure multi-party computation with another participant, obtaining a fragment of second gradient data, where the second gradient data is gradient data obtained after the first gradient data is clipped. Based on the fragment of second gradient data and the fragment of noise data, determining a fragment of third gradient data, where the third gradient data is the second gradient data with the noise data added. A fragment of a model parameter is determined based on the fragment of third gradient data.