Differential privacy (DP), a rigorously quantifiable privacy preservation technique, has found widespread application within the domain of machine learning. As DP techniques are implemented in machine learning algorithms, a significant and intricate trade-off between privacy and utility emerges, garnering extensive attention from researchers. In the pursuit of striking a delicate equilibrium between safeguarding sensitive data and optimizing its utility, researchers have introduced various variants of Relaxed Differential Privacy (RDP) definitions. These nuanced formulations, however, exhibit substantial diversity in their underlying principles and interpretations of the core concept of DP, thereby engendering a current void in the comprehensive synthesis of these related works. The principal objective of this article is twofold. Firstly, it aims to provide a comprehensive summary of pertinent research endeavors pertaining to RDP within the realm of machine learning. Secondly, it endeavors to empirically assess the impact on both privacy and utility stemming from machine learning algorithms founded upon these RDP definitions. Additionally, this article undertakes a systematic analysis of the foundational principles underpinning distinct variants of relaxed definitions, culminating in the development of a taxonomy that categorizes these RDP definitions.

中文翻译：

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对私人学习中的宽松差分隐私进行基准测试：一项比较调查

差分隐私 （DP） 是一种严格可量化的隐私保护技术，已在机器学习领域得到广泛应用。随着 DP 技术在机器学习算法中实现，隐私和实用性之间出现了一个重要而复杂的权衡，引起了研究人员的广泛关注。为了在保护敏感数据和优化其效用之间取得微妙的平衡，研究人员引入了宽松差分隐私 （RDP） 定义的各种变体。然而，这些微妙的表述在它们的基本原则和对 DP 核心概念的解释方面表现出了巨大的差异，从而在这些相关作品的全面综合中产生了当前的空白。本文的主要目标有两个。首先，它旨在全面总结机器学习领域内与 RDP 相关的研究工作。其次，它试图实证评估基于这些 RDP 定义的机器学习算法对隐私和实用性的影响。此外，本文还对支撑松散定义的不同变体的基本原则进行了系统分析，最终开发了对这些 RDP 定义进行分类的分类法。