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In this chapter, we introduce an important line of research dedicated to measuring and providing protection for individual privacy in machine learning. We will start from a common standard for measuring privacy protection, differential privacy, and present related definitions, properties, and extended theories.

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Privacy Preservation

  • 章节

  • 发表日期：

    2025年

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  • Fengxiang He,
  • Dacheng Tao

摘要

In this chapter, we introduce an important line of research dedicated to measuring and providing protection for individual privacy in machine learning. We will start from a common standard for measuring privacy protection, differential privacy, and present related definitions, properties, and extended theories.