Privacy-Preserving Machine Learning: Threats and Solutions

For privacy concerns to be addressed adequately in today's machine-learning (ML) systems, the knowledge gap between the ML and privacy communities must be bridged. This article aims to provide an introduction to the intersection of both fields with special emphasis on the techniques used to protect the data.

• UD
  U.S. Department of Defense
• BU
  Brandeis University
• DA
  Defense Advanced Research Projects Agency