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When working with AI applications and models, the privacy of individuals is of utmost importance. Privacy in machine learning aims to preserve the privacy of individuals represented in the datasets. It tends to employ and use different techniques to safeguard sensitive data during ML development and deployment. In this chapter, we’ll look at privacy and how to measure privacy in ML models and algorithms.

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Privacy

  • 章节

  • 发表日期：

    2025年

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  • Toju Duke,
  • Paolo Giudici

摘要

When working with AI applications and models, the privacy of individuals is of utmost importance. Privacy in machine learning aims to preserve the privacy of individuals represented in the datasets. It tends to employ and use different techniques to safeguard sensitive data during ML development and deployment. In this chapter, we’ll look at privacy and how to measure privacy in ML models and algorithms.