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In this chapter, we introduce classical perturbation-based privacy notions, including differential privacy, identifiability, and mutual-information privacy, and then propose a completeness privacy notions framework linking those notions. This privacy notions framework, a fundamental building block for privacy analysis, is extensively utilized in subsequent chapters. Furthermore, the framework is essential for achieving privacy preservation in crowdsensing systems.

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Perturbation-Based Privacy Preservation

  • 章节

  • 发表日期：

    2026年

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  • Zhirun Zheng,
  • Zhetao Li,
  • Xuemin Shen

摘要

In this chapter, we introduce classical perturbation-based privacy notions, including differential privacy, identifiability, and mutual-information privacy, and then propose a completeness privacy notions framework linking those notions. This privacy notions framework, a fundamental building block for privacy analysis, is extensively utilized in subsequent chapters. Furthermore, the framework is essential for achieving privacy preservation in crowdsensing systems.