Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy. (arXiv:2208.08662v1 [cs.CR])

Secure multi-party computation-based machine learning, referred to as MPL,
has become an important technology to utilize data from multiple parties with
privacy preservation. While MPL provides rigorous security guarantees for the
computation process, the models trained by MPL are still vulnerable to attacks
that solely depend on access to the models. Differential privacy could help to
defend against such attacks. However, the accuracy loss brought by differential
privacy and the huge communication overhead of secure multi-party computation
protocols make it …

differential privacy party privacy