  This paper surveys recent work in the intersection of differential privacy
(DP) and fairness. It reviews the conditions under which privacy and fairness
may have aligned or contrasting goals, analyzes how and why DP may exacerbate
bias and unfairness in decision problems and learning tasks, and describes
available mitigation measures for the fairness issues arising in DP systems.
The survey provides a unified understanding of the main challenges and
potential risks arising when deploying privacy-preserving machine-learning or
decisions-making tasks under a fairness lens.
