Achieving Differential Privacy and Fairness in Logistic Regression
Achieving Differential Privacy and Fairness in Logistic Regression
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DOI：
10.1145/3308560.3317584
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Depeng Xu;Shuhan Yuan;Xintao Wu
中科院分区：
文献类型：
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作者：
Depeng Xu;Shuhan Yuan;Xintao Wu
机器学习算法用于在各种应用中做出决策。这些算法依赖于大量敏感的个人信息才能正常工作。因此，在隐私和公平等问题上，机器学习算法存在社会学担忧。目前，许多研究只关注保护个人隐私或确保算法的公平性。然而，如何在机器学习算法中同时满足隐私和公平性要求还有待开发。在本文中，我们专注于一个经典的机器学习模型，逻辑回归，并开发不同的私人和公平的逻辑回归模型相结合的功能机制和决策边界的公平性在一个联合的形式。理论分析和实证评估表明，我们的方法有效地实现了差分隐私和公平，同时保持良好的效用。
Machine learning algorithms are used to make decisions in various applications. These algorithms rely on large amounts of sensitive individual information to work properly. Hence, there are sociological concerns about machine learning algorithms on matters like privacy and fairness. Currently, many studies focus on only protecting individual privacy or ensuring fairness of algorithms. However, how to meet both privacy and fairness requirements simultaneously in machine learning algorithms is under exploited. In this paper, we focus on one classic machine learning model, logistic regression, and develop differentially private and fair logistic regression models by combining functional mechanism and decision boundary fairness in a joint form. Theoretical analysis and empirical evaluations demonstrate our approaches effectively achieve both differential privacy and fairness while preserving good utility.