mlsanityData to 7 Oct 2026

A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures

arXiv 2404.00673 ↗First posted 31 Mar 2024cs.CR cs.AI cs.CYcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 109 of its 214 references that are in the map. First posted on arXiv 31 Mar 2024.

Builds on

109 of 214 references.

References · 109

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  4. 2016Deep Learning with Differential Privacycited by 2,682
  5. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  6. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  7. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  8. 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
  9. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  10. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  11. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  12. 2014Striving for Simplicity: The All Convolutional Netcited by 983
38 more
  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  2. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  3. 2021Membership Inference Attacks From First Principlescited by 839
  4. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  5. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 731
  6. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  7. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  8. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  9. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  10. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
  11. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 475
  12. 2018A Benchmark for Interpretability Methods in Deep Neural Networkscited by 467
  13. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  14. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  15. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 343
  16. 2018Actionable Recourse in Linear Classificationcited by 327
  17. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 279
  18. 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 268
  19. 2019White-box vs Black-box: Bayes Optimal Strategies for Membership Inferencecited by 265
  20. 2018Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negativescited by 261
  21. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  22. 2015Explaining NonLinear Classification Decisions with Deep Taylor Decompositioncited by 248
  23. 2018Learning to Explain: An Information-Theoretic Perspective on Model Interpretationcited by 248
  24. 2019The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networkscited by 241
  25. 2021Enhanced Membership Inference Attacks against Machine Learning Modelscited by 237
  26. 2017Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictionscited by 234
  27. 2019High Accuracy and High Fidelity Extraction of Neural Networkscited by 208
  28. 2018Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learningcited by 203
  29. 2019MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examplescited by 183
  30. 2015Inverting Visual Representations with Convolutional Networkscited by 168
  31. 2017Machine Learning Models that Remember Too Muchcited by 165
  32. 2019Learning Model-Agnostic Counterfactual Explanations for Tabular Datacited by 146
  33. 2018Explaining Explanations in AIcited by 136
  34. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  35. 2018Differentially Private Empirical Risk Minimization Revisited: Faster and More Generalcited by 127
  36. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  37. 2019Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learningcited by 117
  38. 2019The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasonscited by 115

50 most cited of 109 All 109 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 3.

6 papers.

  1. 2024Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resiliencecited by 8
  2. 2024Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual Explanationscited by 1
  3. 2024Heterogeneous Hypergraph Embedding for Recommendation Systemscited by 1
  4. 2026Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanationscited by 1
  5. 2026Linear Model Extraction via Factual and Counterfactual Queriescited by 1
  6. 2026Mitigating Explanation Leakage in Financial Fraud Detection Systemscited by 0