Please Tell Me More: Privacy Impact of Explainability through the Lens of Membership Inference Attack |
2024 |
SP |
Feature-based |
Membership Inference |
Differential Privacy, Privacy-Preserving Models, DP-SGD |
- |
On the Privacy Risks of Algorithmic Recourse |
2023 |
AISTATS |
Counterfactual |
Membership Inference |
Differential Privacy |
- |
The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks |
2023 |
TIST |
Counterfactual |
Linkage |
Anonymisaion |
- |
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations |
2023 |
KDD |
Counterfactual |
- |
Perturbation |
[Code] |
Private Graph Extraction via Feature Explanations |
2023 |
PETS |
Feature-based |
Graph Extraction |
Perturbation |
[Code] |
Privacy-Preserving Algorithmic Recourse |
2023 |
ICAIF |
Counterfactual |
- |
Differential Privacy |
- |
Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage |
2023 |
ICML-Workshop |
Counterfactual |
Membership Inference |
Differential Privacy |
- |
Probabilistic Dataset Reconstruction from Interpretable Models |
2023 |
arXiv |
Interpretable Surrogates |
Data Reconstruction |
- |
[Code] |
DeepFixCX: Explainable privacy-preserving image compression for medical image analysis |
2023 |
WIREs-DMKD |
Case-based |
Identity recognition |
Anonymisation |
[Code] |
XorSHAP: Privacy-Preserving Explainable AI for Decision Tree Models |
2023 |
Preprint |
Shapley |
- |
Multi-party Computation |
- |
- |
2023 |
Github |
ALE plot |
- |
Differential Privacy |
[Code] |
Inferring Sensitive Attributes from Model Explanations |
2022 |
CIKM |
Gradient-based, Perturbation-based |
Attribute Inference |
- |
[Code] |
Model explanations with differential privacy |
2022 |
FAccT |
Feature-based |
- |
Differential Privacy |
- |
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations |
2022 |
FAccT |
Counterfactual |
Model Extraction |
- |
- |
Feature Inference Attack on Shapley Values |
2022 |
CCS |
Shapley |
Attribute/Feature Inference |
Low-dimensional |
- |
Evaluating the privacy exposure of interpretable global explainers |
2022 |
CogMI |
Interpretable Surrogates |
Membership Inference |
- |
- |
Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy |
2022 |
IEEE Access |
Example-based |
- |
Anonymisation |
- |
On the amplification of security and privacy risks by post-hoc explanations in machine learning models |
2022 |
arXiv |
Feature-based |
Membership Inference |
- |
- |
Differentially Private Counterfactuals via Functional Mechanism |
2022 |
arXiv |
Counterfactual |
- |
Differential Privacy |
- |
Differentially Private Shapley Values for Data Evaluation |
2022 |
arXiv |
Shapley |
- |
Differential Privacy |
[Code] |
Exploiting Explanations for Model Inversion Attacks |
2021 |
ICCV |
Gradient-based, Interpretable Surrogates |
Model Inversion |
- |
- |
On the Privacy Risks of Model Explanations |
2021 |
AIES |
Feature-based, Shapley, Counterfactual |
Membership Inference |
- |
- |
Adversarial XAI Methods in Cybersecurity |
2021 |
TIFS |
Counterfactual |
Membership Inference |
- |
- |
MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI |
2021 |
arXiv |
Gradient-based |
Model Extraction |
- |
[Code] |
Robust Counterfactual Explanations for Privacy-Preserving SVM |
2021 |
ICML-Workshop |
Counterfactual |
- |
Private SVM |
[Code] |
When Differential Privacy Meets Interpretability: A Case Study |
2021 |
RCV-CVPR |
Interpretable Models |
- |
Differential Privacy |
- |
Differentially Private Quantiles |
2021 |
ICML |
Quantiles |
- |
Differential Privacy |
[Code] |
FOX: Fooling with Explanations : Privacy Protection with Adversarial Reactions in Social Media |
2021 |
PST |
- |
Attribute Inference |
Privacy-Protecting Explanation |
- |
Privacy-preserving generative adversarial network for case-based explainability in medical image analysis |
2021 |
IEEE Access |
Example-based |
- |
Generative Anonymisation |
- |
Interpretable and Differentially Private Predictions |
2020 |
AAAI |
Locally linear maps |
- |
Differential Privacy |
[Code] |
Model extraction from counterfactual explanations |
2020 |
arXiv |
Counterfactual |
Model Extraction |
- |
[Code] |
Model Reconstruction from Model Explanations |
2019 |
FAT* |
Gradient-based |
Model Reconstruction, Model Extraction |
- |
- |
Interpret Federated Learning with Shapley Values |
2019 |
- |
Shapley |
- |
Federated |
[Code] |
Collaborative Explanation of Deep Models with Limited Interaction for Trade Secret and Privacy Preservation |
2019 |
WWW |
Feature-based |
- |
Collaborative rule-based model |
- |
Model inversion attacks that exploit confidence information and basic countermeasures |
2015 |
CCS |
Confidence scores |
Reconstruction, Model Inversion |
- |
- |