  Machine Learning (ML) technologies have been widely adopted in many mission
critical fields, such as cyber security, autonomous vehicle control,
healthcare, etc. to support intelligent decision-making. While ML has
demonstrated impressive performance over conventional methods in these
applications, concerns arose with respect to system resilience against
ML-specific security attacks and privacy breaches as well as the trust that
users have in these systems. In this article, firstly we present our recent
systematic and comprehensive survey on the state-of-the-art ML robustness and
trustworthiness technologies from a security engineering perspective, which
covers all aspects of secure ML system development including threat modeling,
common offensive and defensive technologies, privacy-preserving machine
learning, user trust in the context of machine learning, and empirical
evaluation for ML model robustness. Secondly, we then push our studies forward
above and beyond a survey by describing a metamodel we created that represents
the body of knowledge in a standard and visualized way for ML practitioners. We
further illustrate how to leverage the metamodel to guide a systematic threat
analysis and security design process in a context of generic ML system
development, which extends and scales up the classic process. Thirdly, we
propose future research directions motivated by our findings to advance the
development of robust and trustworthy ML systems. Our work differs from
existing surveys in this area in that, to the best of our knowledge, it is the
first of its kind of engineering effort to (i) explore the fundamental
principles and best practices to support robust and trustworthy ML system
development; and (ii) study the interplay of robustness and user trust in the
context of ML systems.
