XRand: Differentially Private Defense against Explanation-Guided Attacks
DOI:
https://doi.org/10.1609/aaai.v37i10.26401Keywords:
PEAI: Privacy and Security, PEAI: Safety, Robustness & TrustworthinessAbstract
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.Downloads
Published
2023-06-26
How to Cite
Nguyen, T., Lai, P., Phan, H., & Thai, M. T. (2023). XRand: Differentially Private Defense against Explanation-Guided Attacks. Proceedings of the AAAI Conference on Artificial Intelligence, 37(10), 11873-11881. https://doi.org/10.1609/aaai.v37i10.26401
Issue
Section
AAAI Technical Track on Philosophy and Ethics of AI