Privacy-preserving explainable AI: a survey
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Huynh, Thanh Trung
Ren, Zhao
Nguyen, Thanh Toan
Nguyen, Phi Le
Yin, Hongzhi
Nguyen, Quoc Viet Hung
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Abstract
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 categorization 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.
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Science China Information Sciences
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68
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DP240101108
DE200101465
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© 2024 The Author(s). Open access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Communications engineering
Control engineering, mechatronics and robotics
Electronics, sensors and digital hardware
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Nguyen, TT; Huynh, TT; Ren, Z; Nguyen, TT; Nguyen, PL; Yin, H; Nguyen, QVH, Privacy-preserving explainable AI: a survey, Science China Information Sciences, 2025, 68, pp. 111101