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Publications
  • April 2023
  • Article
  • Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)

On the Privacy Risks of Algorithmic Recourse

By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
  • | Pages:16
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Abstract

As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected individuals, potential adversaries could also exploit these recourses to compromise privacy. In this work, we make the first attempt at investigating if and how an adversary can leverage recourses to infer private information about the underlying model’s training data. To this end, we propose a series of novel membership inference attacks which leverage algorithmic recourse. More specifically, we extend the prior literature on membership inference attacks to the recourse setting by leveraging the distances between data instances and their corresponding counterfactuals output by state-of-the-art recourse methods. Extensive experimentation with real world and synthetic datasets demonstrates significant privacy leakage through recourses. Our work establishes unintended privacy leakage as an important risk in the widespread adoption of recourse methods.

Keywords

Recourse; Privacy Threats; AI and Machine Learning; Information

Citation

Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 206 (April 2023).
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About The Authors

Himabindu Lakkaraju

Technology and Operations Management
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Seth Neel

Technology and Operations Management
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More from the Authors

    • 2024
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    Fair Machine Unlearning: Data Removal while Mitigating Disparities

    By: Himabindu Lakkaraju, Flavio Calmon, Jiaqi Ma and Alex Oesterling
    • 2024
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    Quantifying Uncertainty in Natural Language Explanations of Large Language Models

    By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
    • 2023
    • Proceedings of the Conference on Empirical Methods in Natural Language Processing

    MoPe: Model Perturbation-based Privacy Attacks on Language Models

    By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
More from the Authors
  • Fair Machine Unlearning: Data Removal while Mitigating Disparities By: Himabindu Lakkaraju, Flavio Calmon, Jiaqi Ma and Alex Oesterling
  • Quantifying Uncertainty in Natural Language Explanations of Large Language Models By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
  • MoPe: Model Perturbation-based Privacy Attacks on Language Models By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
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