A Survey of Privacy-Preserving Model Explanations: Privacy Risks,
  Attacks, and Countermeasures
Thanh Tam Nguyen Thanh Trung Huynh Zhao Ren Thanh Toan Nguyen Phi Le Nguyen Hongzhi Yin Quoc Viet Hung Nguyen
As the adoption of explainable AI (XAI) continues to expand, the urgency to
address its privacy implications intensifies. Despite a growing corpus of
research in AI privacy and explainability, there is little attention on
privacy-preserving model explanations. This article presents the first thorough
survey about privacy attacks on model explanations and their countermeasures.
Our contribution to this field comprises a thorough analysis of research papers
with a connected taxonomy that facilitates the categorisation of privacy
attacks and countermeasures based on the targeted explanations. This work also
includes an initial investigation into the causes of privacy leaks. Finally, we
discuss unresolved issues and prospective research directions uncovered in our
analysis. This survey aims to be a valuable resource for the research community
and offers clear insights for those new to this domain. To support ongoing
research, we have established an online resource repository, which will be
continuously updated with new and relevant findings. Interested readers are
encouraged to access our repository at
https://github.com/tamlhp/awesome-privex.