Secure machine learning is here!
Privacy is so important and is one of the main topics being spoken about, so how do we ensure that we can keep our data private whilst collaborating with different models?
Manohar Jonnalagedda and Jakob Odersky at Scala Days described the unique aspects of writing a compiler so that you don't need to be a crypto expert.
Compiling to preserve our privacy
Move over data analytics, secure machine learning is here. As privacy becomes an increasingly important concern, so does the need to analyze data securely. Secure multi-party computation (MPC) is a promising solution that helps different entities collaborate in training ML models, while also keeping their data private.
For applications to truly scale, we need to implement models in a high-level language, abstracting away the low-level MPC details: we need a compiler! In this talk we describe the unique aspects of writing a compiler so that developers and data scientists need not be crypto experts.
This talk was given by Manohar Jonnalagedda and Jakob Odersky at Scala Days 2019.