research-article

Fairness Audit of Machine Learning Models with Confidential Computing

Authors:

Yeon-sup LimAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 3488 - 3499

https://doi.org/10.1145/3485447.3512244

Published: 25 April 2022 Publication History

Abstract

Algorithmic discrimination is one of the significant concerns in applying machine learning models to a real-world system. Many researchers have focused on developing fair machine learning algorithms without discrimination based on legally protected attributes. However, the existing research has barely explored various security issues that can occur while evaluating model fairness and verifying fair models. In this study, we propose a fairness audit framework that assesses the fairness of ML algorithms while addressing potential security issues such as data privacy, model secrecy, and trustworthiness. To this end, our proposed framework utilizes confidential computing and builds a chain of trust through enclave attestation primitives combined with public scrutiny and state-of-the-art software-based security techniques, enabling fair ML models to be securely certified and clients to verify a certified one. Our micro-benchmarks on various ML models and real-world datasets show the feasibility of the fairness certification implemented with Intel SGX in practice. In addition, we analyze the impact of data poisoning, which is an additional threat during data collection for fairness auditing. Based on the analysis, we illustrate the theoretical curves of fairness gap and minimal group size and the empirical results of fairness certification on poisoned datasets.

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Cited By

Yadav CChowdhury ABoneh DChaudhuri KSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)FairProofProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694365(55682-55705)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694365
Xue JZheng MSheng YYang LLou QJiang LLi BXu WChen JZhang YXue JWang SBai GYuan X(2024)TrojFair: Trojan Fairness AttacksProceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis10.1145/3689217.3690620(47-56)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1145/3689217.3690620
Mo FTarkhani ZHaddadi H(2024)Machine Learning with Confidential Computing: A Systematization of KnowledgeACM Computing Surveys10.1145/367000756:11(1-40)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3670007
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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
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Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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https://dl.acm.org/doi/10.5555/3692070.3694365
Xue JZheng MSheng YYang LLou QJiang LLi BXu WChen JZhang YXue JWang SBai GYuan X(2024)TrojFair: Trojan Fairness AttacksProceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis10.1145/3689217.3690620(47-56)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1145/3689217.3690620
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