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<html>
<head>
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<title>Awesome Privacy-Preserving XAI</title>
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<table width="100%" align="center" border="0" cellpadding="0" cellspacing="0">
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<tr>
<td class="mytitle">A Survey of Privacy-Preserving Model Explanations</td>
</tr>
<tr>
<td class="subject" style="padding-top:0px; color: #000;">
<b>Awesome Privacy-Preserving Explainable AI</b>
</td>
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<tr bgcolor="#FF0000">
<td height="1px" style="padding:2px">
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<td class="subTitle" style="padding-top:25px">
I. Introduction
</td>
</tr>
<tr>
<td class="body">
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.
</td>
</tr>
<tr>
<td>
<img class="center" src="images/taxonomy1.png" alt="" border=0></img>
</td>
</tr>
<tr>
<td class="subTitle">
II. List of Approaches (Sortable)
</td>
</tr>
<tr>
<td class="body" id="totalRows">Total number of rows: XX</td>
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<tr>
<td>
<table id="publicationList" class="sortable" style="padding-left:25px">
<thead>
<tr>
<th class=""><span>Title</span></th>
<th class="" id="year">Year</th>
<th class="">Venue</th>
<th class="">Target Explanations</th>
<th class="">Attacks</th>
<th class="">Defenses</th>
<th class="">Code</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://www.computer.org/csdl/proceedings-article/sp/2024/313000a120/1Ub23teQ7PG"
rel="nofollow">Please Tell Me More: Privacy Impact of
Explainability through the Lens of Membership Inference
Attack</a></td>
<td align="center">2024</td>
<td><em>SP</em></td>
<td align="center">Feature-based</td>
<td align="center">Membership Inference</td>
<td align="center">Differential Privacy, Privacy-Preserving Models,
DP-SGD</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://arxiv.org/abs/2404.07139"
rel="nofollow">Towards a Game-theoretic Understanding of
Explanation-based Membership Inference Attacks</a></td>
<td align="center">2024</td>
<td><em>arXiv</em></td>
<td align="center">Feature-based</td>
<td align="center">Membership Inference</td>
<td align="center">Game Theory</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://proceedings.mlr.press/v206/pawelczyk23a.html"
rel="nofollow">On the Privacy Risks of Algorithmic
Recourse</a></td>
<td align="center">2023</td>
<td><em>AISTATS</em></td>
<td align="center">Counterfactual</td>
<td align="center">Membership Inference</td>
<td align="center">Differential Privacy</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://dl.acm.org/doi/full/10.1145/3608482"
rel="nofollow">The Privacy Issue of Counterfactual
Explanations: Explanation Linkage Attacks</a></td>
<td align="center">2023</td>
<td><em>TIST</em></td>
<td align="center">Counterfactual</td>
<td align="center">Linkage</td>
<td align="center">Anonymisaion</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://dl.acm.org/doi/abs/10.1145/3580305.3599343"
rel="nofollow">Feature-based Learning for Diverse and
Privacy-Preserving Counterfactual Explanations</a></td>
<td align="center">2023</td>
<td><em>KDD</em></td>
<td align="center">Counterfactual</td>
<td align="center">-</td>
<td align="center">Perturbation</td>
<td align="center"><a
href="https://github.com/isVy08/L2C/">[Code]</a></td>
</tr>
<tr>
<td><a href="https://petsymposium.org/popets/2023/popets-2023-0041.pdf"
rel="nofollow">Private Graph Extraction via Feature
Explanations</a></td>
<td align="center">2023</td>
<td><em>PETS</em></td>
<td align="center">Feature-based</td>
<td align="center">Graph Extraction</td>
<td align="center">Perturbation</td>
<td align="center"><a
href="https://github.com/iyempissy/graph-stealing-attacks-with-explanation">[Code]</a>
</td>
</tr>
<tr>
<td><a href="https://arxiv.org/abs/2311.14137"
rel="nofollow">Privacy-Preserving Algorithmic Recourse</a>
</td>
<td align="center">2023</td>
<td><em>ICAIF</em></td>
<td align="center">Counterfactual</td>
<td align="center">-</td>
<td align="center">Differential Privacy</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://arxiv.org/abs/2308.04341"
rel="nofollow">Accurate, Explainable, and Private Models:
Providing Recourse While Minimizing Training Data
Leakage</a></td>
<td align="center">2023</td>
<td><em>ICML-Workshop</em></td>
<td align="center">Counterfactual</td>
<td align="center">Membership Inference</td>
<td align="center">Differential Privacy</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://arxiv.org/abs/2308.15099"
rel="nofollow">Probabilistic Dataset Reconstruction from
Interpretable Models</a></td>
<td align="center">2023</td>
<td><em>arXiv</em></td>
<td align="center">Interpretable Surrogates</td>
<td align="center">Data Reconstruction</td>
<td align="center">-</td>
<td align="center"><a
href="https://github.com/ferryjul/ProbabilisticDatasetsReconstruction">[Code]</a>
</td>
</tr>
<tr>
<td><a href="https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1495"
rel="nofollow">DeepFixCX: Explainable privacy-preserving
image compression for medical image analysis</a></td>
<td align="center">2023</td>
<td><em>WIREs-DMKD</em></td>
<td align="center">Case-based</td>
<td align="center">Identity recognition</td>
<td align="center">Anonymisation</td>
<td align="center"><a
href="https://github.com/adgaudio/DeepFixCX">[Code]</a></td>
</tr>
<tr>
<td><a href="https://eprint.iacr.org/2023/1859"
rel="nofollow">XorSHAP: Privacy-Preserving Explainable AI
for Decision Tree Models</a></td>
<td align="center">2023</td>
<td><em>Preprint</em></td>
<td align="center">Shapley</td>
<td align="center">-</td>
<td align="center">Multi-party Computation</td>
<td align="center">-</td>
</tr>
<tr>
<td>DP-XAI</td>
<td align="center">2023</td>
<td><em>Github</em></td>
<td align="center">ALE plot</td>
<td align="center">-</td>
<td align="center">Differential Privacy</td>
<td align="center"><a
href="https://github.com/lange-martin/dp-global-xai">[Code]</a>
</td>
</tr>
<tr>
<td><a href="https://dl.acm.org/doi/abs/10.1145/3511808.3557362"
rel="nofollow">Inferring Sensitive Attributes from Model
Explanations</a></td>
<td align="center">2022</td>
<td><em>CIKM</em></td>
<td align="center">Gradient-based, Perturbation-based</td>
<td align="center">Attribute Inference</td>
<td align="center">-</td>
<td align="center"><a
href="https://github.com/vasishtduddu/AttInfExplanations">[Code]</a>
</td>
</tr>
<tr>
<td><a href="https://dl.acm.org/doi/abs/10.1145/3531146.3533235"
rel="nofollow">Model explanations with differential
privacy</a></td>
<td align="center">2022</td>
<td><em>FAccT</em></td>
<td align="center">Feature-based</td>
<td align="center">-</td>
<td align="center">Differential Privacy</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://dl.acm.org/doi/10.1145/3531146.3533188"
rel="nofollow">DualCF: Efficient Model Extraction Attack
from Counterfactual Explanations</a></td>
<td align="center">2022</td>
<td><em>FAccT</em></td>
<td align="center">Counterfactual</td>
<td align="center">Model Extraction</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://dl.acm.org/doi/abs/10.1145/3548606.3560573"
rel="nofollow">Feature Inference Attack on Shapley
Values</a></td>
<td align="center">2022</td>
<td><em>CCS</em></td>
<td align="center">Shapley</td>
<td align="center">Attribute/Feature Inference</td>
<td align="center">Low-dimensional</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://ieeexplore.ieee.org/abstract/document/10063510/"
rel="nofollow">Evaluating the privacy exposure of
interpretable global explainers</a>, <a
href="https://ebooks.iospress.nl/doi/10.3233/FAIA220206"
rel="nofollow">Privacy Risk of Global Explainers</a></td>
<td align="center">2022</td>
<td><em>CogMI</em></td>
<td align="center">Interpretable Surrogates</td>
<td align="center">Membership Inference</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://ieeexplore.ieee.org/document/9729808/"
rel="nofollow">Privacy-Preserving Case-Based Explanations:
Enabling Visual Interpretability by Protecting Privacy</a>
</td>
<td align="center">2022</td>
<td><em>IEEE Access</em></td>
<td align="center">Example-based</td>
<td align="center">-</td>
<td align="center">Anonymisation</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://arxiv.org/abs/2206.14004" rel="nofollow">On the
amplification of security and privacy risks by post-hoc
explanations in machine learning models</a></td>
<td align="center">2022</td>
<td><em>arXiv</em></td>
<td align="center">Feature-based</td>
<td align="center">Membership Inference</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://arxiv.org/abs/2208.02878"
rel="nofollow">Differentially Private Counterfactuals via
Functional Mechanism</a></td>
<td align="center">2022</td>
<td><em>arXiv</em></td>
<td align="center">Counterfactual</td>
<td align="center">-</td>
<td align="center">Differential Privacy</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://arxiv.org/abs/2206.00511"
rel="nofollow">Differentially Private Shapley Values for
Data Evaluation</a></td>
<td align="center">2022</td>
<td><em>arXiv</em></td>
<td align="center">Shapley</td>
<td align="center">-</td>
<td align="center">Differential Privacy</td>
<td align="center"><a
href="https://github.com/amiratag/DataShapley">[Code]</a>
</td>
</tr>
<tr>
<td><a href="https://openaccess.thecvf.com/content/ICCV2021/html/Zhao_Exploiting_Explanations_for_Model_Inversion_Attacks_ICCV_2021_paper.html"
rel="nofollow">Exploiting Explanations for Model Inversion
Attacks</a></td>
<td align="center">2021</td>
<td><em>ICCV</em></td>
<td align="center">Gradient-based, Interpretable Surrogates</td>
<td align="center">Model Inversion</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://dl.acm.org/doi/abs/10.1145/3461702.3462533"
rel="nofollow">On the Privacy Risks of Model
Explanations</a></td>
<td align="center">2021</td>
<td>AIES</td>
<td align="center">Feature-based, Shapley, Counterfactual</td>
<td align="center">Membership Inference</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://ieeexplore.ieee.org/abstract/document/9555622"
rel="nofollow">Adversarial XAI Methods in Cybersecurity</a>
</td>
<td align="center">2021</td>
<td>TIFS</td>
<td align="center">Counterfactual</td>
<td align="center">Membership Inference</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://arxiv.org/abs/2107.08909" rel="nofollow">MEGEX:
Data-Free Model Extraction Attack against Gradient-Based
Explainable AI</a></td>
<td align="center">2021</td>
<td><em>arXiv</em></td>
<td align="center">Gradient-based</td>
<td align="center">Model Extraction</td>
<td align="center">-</td>
<td align="center"><a
href="https://github.com/cake-lab/datafree-model-extraction">[Code]</a>
</td>
</tr>
<tr>
<td><a href="https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1581005&dswid=5229"
rel="nofollow">Robust Counterfactual Explanations for
Privacy-Preserving SVM</a>, <a
href="https://arxiv.org/abs/2102.03785"
rel="nofollow">Robust Explanations for Private Support
Vector Machines</a></td>
<td align="center">2021</td>
<td><em>ICML-Workshop</em></td>
<td align="center">Counterfactual</td>
<td align="center">-</td>
<td align="center">Private SVM</td>
<td align="center"><a
href="https://github.com/rami-mochaourab/robust-explanation-SVM">[Code]</a>
</td>
</tr>
<tr>
<td><a href="https://arxiv.org/abs/2106.13203" rel="nofollow">When
Differential Privacy Meets Interpretability: A Case
Study</a></td>
<td align="center">2021</td>
<td><em>RCV-CVPR</em></td>
<td align="center">Interpretable Models</td>
<td align="center">-</td>
<td align="center">Differential Privacy</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://proceedings.mlr.press/v139/gillenwater21a.html"
rel="nofollow">Differentially Private Quantiles</a></td>
<td align="center">2021</td>
<td><em>ICML</em></td>
<td align="center">Quantiles</td>
<td align="center">-</td>
<td align="center">Differential Privacy</td>
<td align="center"><a
href="https://github.com/google-research/google-research/tree/master/dp_multiq">[Code]</a>
</td>
</tr>
<tr>
<td><a href="https://ieeexplore.ieee.org/document/9647778"
rel="nofollow">FOX: Fooling with Explanations : Privacy
Protection with Adversarial Reactions in Social Media</a>
</td>
<td align="center">2021</td>
<td><em>PST</em></td>
<td align="center">-</td>
<td align="center">Attribute Inference</td>
<td align="center">Privacy-Protecting Explanation</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://ieeexplore.ieee.org/abstract/document/9598877/"
rel="nofollow">Privacy-preserving generative adversarial
network for case-based explainability in medical image
analysis</a></td>
<td align="center">2021</td>
<td><em>IEEE Access</em></td>
<td align="center">Example-based</td>
<td align="center">-</td>
<td align="center">Generative Anonymisation</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://ojs.aaai.org/index.php/AAAI/article/view/5827"
rel="nofollow">Interpretable and Differentially Private
Predictions</a></td>
<td align="center">2020</td>
<td><em>AAAI</em></td>
<td align="center">Locally linear maps</td>
<td align="center">-</td>
<td align="center">Differential Privacy</td>
<td align="center"><a
href="https://github.com/frhrdr/dp-llm">[Code]</a></td>
</tr>
<tr>
<td><a href="https://arxiv.org/abs/2009.01884" rel="nofollow">Model
extraction from counterfactual explanations</a></td>
<td align="center">2020</td>
<td><em>arXiv</em></td>
<td align="center">Counterfactual</td>
<td align="center">Model Extraction</td>
<td align="center">-</td>
<td align="center"><a
href="https://github.com/aivodji/mrce">[Code]</a></td>
</tr>
<tr>
<td><a href="https://dl.acm.org/doi/10.1145/3287560.3287562"
rel="nofollow">Model Reconstruction from Model
Explanations</a></td>
<td align="center">2019</td>
<td><em>FAT*</em></td>
<td align="center">Gradient-based</td>
<td align="center">Model Reconstruction, Model Extraction</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://arxiv.org/abs/1905.04519"
rel="nofollow">Interpret Federated Learning with Shapley
Values</a></td>
<td align="center">2019</td>
<td>__</td>
<td align="center">Shapley</td>
<td align="center">-</td>
<td align="center">Federated</td>
<td align="center"><a
href="https://github.com/crownpku/federated_shap">[Code]</a>
</td>
</tr>
<tr>
<td><a href="https://dl.acm.org/doi/10.1145/3308560.3317586"
rel="nofollow">Collaborative Explanation of Deep Models with
Limited Interaction for Trade Secret and Privacy
Preservation</a></td>
<td align="center">2019</td>
<td><em>WWW</em></td>
<td align="center">Feature-based</td>
<td align="center">-</td>
<td align="center">Collaborative rule-based model</td>
<td align="center">-</td>
</tr>
<tr>
<td><a href="https://dl.acm.org/doi/abs/10.1145/2810103.2813677"
rel="nofollow">Model inversion attacks that exploit
confidence information and basic countermeasures</a></td>
<td align="center">2015</td>
<td><em>CCS</em></td>
<td align="center">Confidence scores</td>
<td align="center">Reconstruction, Model Inversion</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
</tbody>
</table>
</td>
</tr>
<!-- End of the table -->
<tr>
<td class="subTitle">
III. Citations
</td>
</tr>
<tr>
<td class="item" style="padding-top: 10px;">
<span><b>Source:</b></span> <a target="_blank"
href="https://github.com/tamlhp/awesome-privex">https://github.com/tamlhp/awesome-privex</a>
</td>
</tr>
<tr>
<td class="item" style="padding-top: 10px;">
<span><b>Paper: </b></span> <a target="_blank"
href="https://arxiv.org/abs/2404.00673">https://arxiv.org/abs/2404.00673</a>
</td>
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