The paper

• Developing Privacy-preserving AI Systems: The Lessons learned
  by Huili Chen, Siam Umar Hussain, Fabian Boemer, Emmanuel Stapf, Ahmad Reza Sadeghi, Farinaz Koushanfar, and Rosario Cammarota

has been accepted for publication at the top conference 57th ACM/IEEE Design Automation Conference (DAC) 2020.

Abstract

Advances in customers' data privacy laws create pressures and pain points across the entire lifecycle of AI products. Working figures such as data scientists and data engineers need to account for the correct use of privacy-enhancing technologies such as homomorphic encryption, secure multi-party computation, and trusted execution environment when they develop, test and deploy products embedding AI models while providing data protection guarantees. In this work, we share the lessons learned during the development of frameworks to aid data scientists and data engineers to map their optimized workloads onto privacy-enhancing technologies seamlessly and correctly.