公平驱动的私有协作机器学习

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Dana Pessach, Tamir Tassa, Erez Shmueli
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引用次数: 0

摘要

在较大的数据集上进行训练,机器学习算法的性能就会大大提高。在医学和金融等许多领域,如果多方合作并共享数据,就能获得更大的数据集。然而,这种数据共享带来了巨大的隐私挑战。虽然近期有多项研究调查了隐私协作机器学习的方法,但这种协作算法的公平性却被忽视了。在这项工作中,我们提出了一种可行的隐私保护预处理机制,以提高协作机器学习算法的公平性。对所提方法的广泛评估表明,该方法能显著提高公平性,而准确性只受到轻微影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fairness-Driven Private Collaborative Machine Learning

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 was 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.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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