New Problem:

  1. Wang, Yongjie, Hangwei Qian, and Chunyan Miao. "Dualcf: Efficient model extraction attack from counterfactual explanations." Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 2022.
  2. Aïvodji, Ulrich, Alexandre Bolot, and Sébastien Gambs. "Model extraction from counterfactual explanations." arXiv preprint arXiv:2009.01884 (2020).
  3. Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the privacy risks of algorithmic recourse." International Conference on Artificial Intelligence and Statistics. PMLR, 2023
  4. Luo, Xinjian, Yangfan Jiang, and Xiaokui Xiao. "Feature inference attack on shapley values." Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. 2022.
  5. Shokri, Reza, Martin Strobel, and Yair Zick. "On the privacy risks of model explanations." Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. 2021.
  6. Duddu, Vasisht, and Antoine Boutet. "Inferring sensitive attributes from model explanations." Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022.
  7. Goethals, Sofie, Kenneth Sörensen, and David Martens. "The privacy issue of counterfactual explanations: explanation linkage attacks." ACM Transactions on Intelligent Systems and Technology 14.5 (2023): 1-24.
  8. Mothilal, Ramaravind K., Amit Sharma, and Chenhao Tan. "Explaining machine learning classifiers through diverse counterfactual explanations." Proceedings of the 2020 conference on fairness, accountability, and transparency. 2020.
  9. Karimi, Amir-Hossein, Bernhard Schölkopf, and Isabel Valera. "Algorithmic recourse: from counterfactual explanations to interventions." Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 2021.
  10. Kuppa, Aditya, and Nhien-An Le-Khac. "Adversarial XAI methods in cybersecurity." IEEE transactions on information forensics and security 16 (2021): 4924-4938.
  11. Slack, Dylan, et al. "Counterfactual explanations can be manipulated." Advances in neural information processing systems 34 (2021): 62-75.
  12. Nguyen, Thanh Tam, et al. "A survey of privacy-preserving model explanations: Privacy risks, attacks, and countermeasures." arXiv preprint arXiv:2404.00673 (2024).
  13. Guidotti, Riccardo. "Counterfactual explanations and how to find them: literature review and benchmarking." Data Mining and Knowledge Discovery 38.5 (2024): 2770-2824.

Old Problem

  1. Koskela, Antti, and Jafar Mohammadi. "Black Box Differential Privacy Auditing Using Total Variation Distance." arXiv preprint arXiv:2406.04827 (2024).