  Recent development in the field of explainable artificial intelligence (XAI)
has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in
which an explanation is provided together with the model prediction in response
to each query. However, XAI also opens a door for adversaries to gain insights
into the black-box models in MLaaS, thereby making the models more vulnerable
to several attacks. For example, feature-based explanations (e.g., SHAP) could
expose the top important features that a black-box model focuses on. Such
disclosure has been exploited to craft effective backdoor triggers against
malware classifiers. To address this trade-off, we introduce a new concept of
achieving local differential privacy (LDP) in the explanations, and from that
we establish a defense, called XRand, against such attacks. We show that our
mechanism restricts the information that the adversary can learn about the top
important features, while maintaining the faithfulness of the explanations.
