As the paradigm of Human-Centered AI (HCAI) gains prominence, its benefits to society are accompanied by significant ethical concerns, one of which is the protection of individual privacy. This chapter provides a comprehensive overview of privacy within HCAI, proposing a human-centered privacy (HCP) framework, providing integrated solution from technology, ethics, and human factors perspectives. The chapter begins by mapping privacy risks across each stage of AI development lifecycle, from data collection to deployment and reuse, highlighting the impact of privacy risks on the entire system. The chapter then introduces privacy-preserving techniques such as federated learning and dif erential privacy. Subsequent chapters integrate the crucial user perspective by examining mental models, alongside the evolving regulatory and ethical landscapes as well as privacy governance. Next, advice on design guidelines is provided based on the human-centered privacy framework. After that, we introduce practical case studies across diverse fields. Finally, the chapter discusses persistent open challenges and future research directions, concluding that a multidisciplinary approach, merging technical, design, policy, and ethical expertise, is essential to successfully embed privacy into the core of HCAI, thereby ensuring these technologies advance in a manner that respects and ensures human autonomy, trust and dignity.

翻译：随着以人为中心的人工智能（HCAI）范式日益受到重视，其在为社会带来益处的同时也伴随着重大的伦理关切，其中之一便是个人隐私的保护。本章全面概述了HCAI中的隐私问题，提出了一个以人为中心的隐私保护（HCP）框架，从技术、伦理和人因视角提供了综合解决方案。本章首先梳理了人工智能开发全生命周期（从数据收集到部署与再利用）各阶段的隐私风险，强调了隐私风险对整个系统的影响。随后，本章介绍了联邦学习与差分隐私等隐私保护技术。后续章节通过考察心智模型，并结合不断演变的监管与伦理环境以及隐私治理，整合了至关重要的用户视角。接着，基于以人为中心的隐私保护框架提供了设计指南建议。之后，我们介绍了跨不同领域的实际案例研究。最后，本章探讨了持续存在的开放性挑战与未来研究方向，并得出结论：必须采用融合技术、设计、政策与伦理专长的多学科方法，才能成功将隐私嵌入HCAI的核心，从而确保这些技术以尊重和保障人类自主性、信任与尊严的方式向前发展。