Accurate, Explainable, and Private Models: Providing Recourse While
  Minimizing Training Data Leakage
Catherine Huang Chelse Swoopes Christina Xiao Jiaqi Ma Himabindu Lakkaraju
Machine learning models are increasingly utilized across impactful domains to
predict individual outcomes. As such, many models provide algorithmic recourse
to individuals who receive negative outcomes. However, recourse can be
leveraged by adversaries to disclose private information. This work presents
the first attempt at mitigating such attacks. We present two novel methods to
generate differentially private recourse: Differentially Private Model (DPM)
and Laplace Recourse (LR). Using logistic regression classifiers and real world
and synthetic datasets, we find that DPM and LR perform well in reducing what
an adversary can infer, especially at low FPR. When training dataset size is
large enough, we find particular success in preventing privacy leakage while
maintaining model and recourse accuracy with our novel LR method.