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In this chapter, we introduce Differential Privacy (DP), a mathematical framework designed to preserve individual privacy in data analysis by incorporating carefully calibrated noise into computations. We provide an intuitive understanding of its principles and delve into its formal definitions, fundamental mechanisms, and composition theorems. Additionally, we discuss its variations, including Relaxed Differential Privacy—such as Rényi Differential Privacy (RDP) and Zero-Concentrated Differential Privacy (zCDP)—which offer greater flexibility and tighter bound for iterative algorithms and complex data analysis tasks. Furthermore, we examine Local Differential Privacy (LDP), a decentralized model that ensures privacy at the data source without the need for a trusted central curator, and highlight its key mechanisms. Finally, we explore widely used tools and libraries for implementing differential privacy and provided examples to illustrate their practical application.

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Differential Privacy

  • 章节

  • 发表日期：

    2025年

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  • Xun Yi,
  • Xuechao Yang,
  • Xiaoning Liu,
  • Andrei Kelarev,
  • Kwok-Yan Lam,
  • Mengmeng Yang,
  • Xiangning Wang,
  • Elisa Bertino

摘要

In this chapter, we introduce Differential Privacy (DP), a mathematical framework designed to preserve individual privacy in data analysis by incorporating carefully calibrated noise into computations. We provide an intuitive understanding of its principles and delve into its formal definitions, fundamental mechanisms, and composition theorems. Additionally, we discuss its variations, including Relaxed Differential Privacy—such as Rényi Differential Privacy (RDP) and Zero-Concentrated Differential Privacy (zCDP)—which offer greater flexibility and tighter bound for iterative algorithms and complex data analysis tasks. Furthermore, we examine Local Differential Privacy (LDP), a decentralized model that ensures privacy at the data source without the need for a trusted central curator, and highlight its key mechanisms. Finally, we explore widely used tools and libraries for implementing differential privacy and provided examples to illustrate their practical application.