43 research outputs found
Evaluating Trade-offs in Computer Vision Between Attribute Privacy, Fairness and Utility
This paper investigates to what degree and magnitude tradeoffs exist between
utility, fairness and attribute privacy in computer vision. Regarding privacy,
we look at this important problem specifically in the context of attribute
inference attacks, a less addressed form of privacy. To create a variety of
models with different preferences, we use adversarial methods to intervene on
attributes relating to fairness and privacy. We see that that certain tradeoffs
exist between fairness and utility, privacy and utility, and between privacy
and fairness. The results also show that those tradeoffs and interactions are
more complex and nonlinear between the three goals than intuition would
suggest