Abstract: Recently, there has been increased interest in collaborative machine learning, where multiple organizations run a training or a prediction task over the joint input data from everyone. Collaboration can be very advantageous because many learning tasks can benefit from learning on complementary datasets or larger datasets. However, when the underlying dataset is sensitive, these organizations cannot collaborate: sensitive data cannot be shared due to factors like privacy concerns, regulatory policies, and/or business competition. A promising paradigm for addressing this problem is provided by secure multi-party computation (MPC), a classic cryptographic technique that allows multiple parties to compute a generic function on everyone’s inputs without revealing the inputs. At a high level, MPC runs a computation on encrypted inputs and produces an encrypted final result that the participants can then jointly decrypt. In this talk, I will give an overview of some current and exciting research in secure collaborative learning using MPC.
