Fairness-Driven Private Collaborative Machine Learning

Dana Pessach

doi:10.1145/3639368

Fairness-Driven Private Collaborative Machine Learning

2024-02-22

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DOI：10.1145/3639368

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Abstract

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The performance of machine learning algorithms can be considerably improved when trained over larger datasets. In many domains, such as medicine and finance, larger datasets can be obtained if several parties, each having access to limited amounts of data, collaborate and share their data. However, such data sharing introduces significant privacy challenges. While multiple recent studies have investigated methods for private collaborative machine learning, the fairness of such collaborative algorithms has been overlooked. In this work, we suggest a feasible privacy-preserving pre-process mechanism for enhancing fairness of collaborative machine learning algorithms. An extensive evaluation of the proposed method shows that it is able to enhance fairness considerably with only a minor compromise in accuracy.

Keywords:

Privacy

algorithmic fairness

collaborative machine learning

federated learning

secure multi-party computation

Fairness-Driven Private Collaborative Machine Learning

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