Privacy constraints in regularized convex optimization

Privacy concerns are becoming more important as more personal data moves online. It is therefore of interest to develop versions of data-processing algorithms which can be guaranteed to preserve the privacy of individuals' data. A general method for creating a privacy-preserving version of a convex optimization problem is described, along with applications to machine learning, statistics, and resource allocation problems.