A Library for Private Machine Learning in TensorFlow — with Jason Mancuso at Dropout Labs. Privacy-preserving machine learning offers many benefits and interesting applications: being able to train and predict on data while it remains in encrypted form unlocks the utility of data that were previously inaccessible due to privacy concerns. …

and an open source Rust library — with Mario Cornejo and Mathieu Poumeyrol at Snips. Today we’re open sourcing our experimental implementation of the Paillier homomorphic encryption scheme, written in Rust by our small team at Snips working on various privacy enhancing technologies. Testing its performance we are not only interested in the concrete numbers we can…

and an open source Rust library — Today we’re happy to announce the open sourcing of our lightweight pure-Rust library for high-volume secret sharing! There already exist many implementations of what’s called Shamir’s secret sharing, but it turned out that for sharing a high volume of secrets, this is not always the best choice. As a result…

by Morten Dahl and Joseph Dureau @ Snips — Awareness, industry standards, as well as legislation around privacy are making steady progress, turning privacy into a strategic and ethical positioning. Large companies, such as Google and Apple, have taken into account these opportunities and constraints, and are setting high standards in the field. Although there are some pragmatic solutions…