On the antagonism of explainability and privacy: A comparative study of attacks and explainers

As explainable artificial intelligence (XAI) becomes more prevalent, concerns arise about the unintended privacy risks associated with model explanations. In this paper, we study the antagonism between explainability and privacy by evaluating the extent to which post-hoc explanations can leak sensitive data. We perform a comparative analysis of three popular XAI methods (SHAP, LIME, and DiCE) applied to Decision Trees, Random Forests, and Neural Networks. We focus on two types of privacy attacks: Membership Inference Attacks and Training Data Extraction. Using datasets of varying complexity, we measure attack success rates and information leakage from explanations. Our results shows that each proposed membership inference attack and training data extraction attack are feasible. These findings highlight the urgent need to design privacy-preserving explainability tools that balance interpretability with user data protection.