  As machine learning becomes a more mainstream technology, the objective for
governments and public sectors is to harness the power of machine learning to
advance their mission by revolutionizing public services. Motivational
government use cases require special considerations for implementation given
the significance of the services they provide. Not only will these applications
be deployed in a potentially hostile environment that necessitates protective
mechanisms, but they are also subject to government transparency and
accountability initiatives which further complicates such protections.
  In this paper, we describe how the inevitable interactions between a user of
unknown trustworthiness and the machine learning models, deployed in
governments and public sectors, can jeopardize the system in two major ways: by
compromising the integrity or by violating the privacy. We then briefly
overview the possible attacks and defense scenarios, and finally, propose
recommendations and guidelines that once considered can enhance the security
and privacy of the provided services.
