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DOI: 10.14569/IJACSA.2023.0140294
PDF

Privacy-Preserving and Trustless Verifiable Fairness Audit of Machine Learning Models

Author 1: Gui Tang
Author 2: Wuzheng Tan
Author 3: Mei Cai

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 2, 2023.

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Abstract: In the big data era, machine learning has devel-oped prominently and is widely used in real-world systems. Yet, machine learning raises fairness concerns, which incurs discrimination against groups determined by sensitive attributes such as gender and race. Many researchers have focused on developing fairness audit technique of machine learning model that enable users to protect themselves from discrimination. Existing solutions, however, rely on additional external trust as-sumptions, either on third-party entities or external components, that significantly lower the security. In this study, we propose a trustless verifiable fairness audit framework that assesses the fairness of ML algorithms while addressing potential security issues such as data privacy, model secrecy, and trustworthiness. With succinctness and non-interactive of zero knowledge proof, our framework not only guarantees audit integrity, but also clearly enhance security, enabling fair ML models to be publicly auditable and any client to verify audit results without extra trust assumption. Our evaluation on various machine learning models and real-world datasets shows that our framework achieves practical performance.

Keywords: Security and privacy; machine learning; fairness; cryptography; zero knowledge proof

Gui Tang, Wuzheng Tan and Mei Cai, “Privacy-Preserving and Trustless Verifiable Fairness Audit of Machine Learning Models” International Journal of Advanced Computer Science and Applications(IJACSA), 14(2), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140294

@article{Tang2023,
title = {Privacy-Preserving and Trustless Verifiable Fairness Audit of Machine Learning Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140294},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140294},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Gui Tang and Wuzheng Tan and Mei Cai}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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