  Security, privacy, and fairness have become critical in the era of data
science and machine learning. More and more we see that achieving universally
secure, private, and fair systems is practically impossible. We have seen for
example how generative adversarial networks can be used to learn about the
expected private training data; how the exploitation of additional data can
reveal private information in the original one; and how what looks like
unrelated features can teach us about each other. Confronted with this
challenge, in this paper we open a new line of research, where the security,
privacy, and fairness is learned and used in a closed environment. The goal is
to ensure that a given entity (e.g., the company or the government), trusted to
infer certain information with our data, is blocked from inferring protected
information from it. For example, a hospital might be allowed to produce
diagnosis on the patient (the positive task), without being able to infer the
gender of the subject (negative task). Similarly, a company can guarantee that
internally it is not using the provided data for any undesired task, an
important goal that is not contradicting the virtually impossible challenge of
blocking everybody from the undesired task. We design a system that learns to
succeed on the positive task while simultaneously fail at the negative one, and
illustrate this with challenging cases where the positive task is actually
harder than the negative one being blocked. Fairness, to the information in the
negative task, is often automatically obtained as a result of this proposed
approach. The particular framework and examples open the door to security,
privacy, and fairness in very important closed scenarios, ranging from private
data accumulation companies like social networks to law-enforcement and
hospitals.
