Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent.

We show how multiple data-owning parties can collaboratively train several machine learning algorithms without jeopardizing the privacy of their sensitive data. In particular, we assume that every party knows specific features of an overlapping set of people. Using a secure implementation of an advanced hidden set intersection protocol and a privacy-preserving Gradient Descent algorithm, we are able to train a Ridge, LASSO or SVM model over the intersection of people in their data sets. Both the hidden set intersection protocol and privacy-preserving LASSO implementation are unprecedented in literature.