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.

本研究采用机器学习模型，在负面结果的情况下提供算法补救措施，并通过两种新颖方法生成差分隐私补救措施：差分隐私模型（DPM）和拉普拉斯补救措施（LR）。使用逻辑回归分类器和真实数据集以及合成数据集，发现DPM和LR在降低对手推断信息方面表现良好，特别是在低假正率的情况下。当训练数据集足够大时，通过我们的新颖LR方法，成功地防止了隐私泄露，同时保持模型和补救措施的准确性。

提供追索权同时最小化训练数据泄露的准确、可解释和私密模型