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.
PrivacyMotiv: Vulnerability-Centered Persona Journeys for Empathic Privacy Reviews in UX Design
Northeastern University（东北大学）；Institute of Design (ID) at Illinois Tech（伊利诺伊理工学院设计学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Notre Dame（圣母大学）
Generative clinical time series models trained on moderate amounts of patient data are privacy preserving
Mathematical Modelling and Data Analysis Department, Physikalisch-Technische Bundesanstalt (PTB)（物理技术联邦机构（PTB）数学建模与数据分析部门）；Institute of Medical Informatics, Charité – Universitätsmedizin Berlin（柏林查理医院医学信息学研究所）
Achieving Hilbert-Schmidt Independence Under Rényi Differential Privacy for Fair and Private Data Generation
Department of Mathematics and Computer Science University of Southern Denmark（丹麦南部大学数学与计算机科学系）；Department of Mathematics and Computer Science Freie Universität Berlin（柏林自由大学数学与计算机科学系）；Faculty for Informatik Universität der Bundeswehr München（联邦国防军大学慕尼黑信息学院）
Toward a Human-Centered Evaluation Framework for Trustworthy LLM-Powered GUI Agents
University of Notre Dame（圣约翰大学）；Northeastern University（东北大学）；Virginia Tech（弗吉尼亚理工大学）；University of Washington（华盛顿大学）；Johns Hopkins University（约翰霍普金斯大学）
From "Fail Fast" to "Mature Safely:" Expert Perspectives as Secondary Stakeholders on Teen-Centered Social Media Risk Detection
University of Cincinnati（辛辛那提大学）；King AbdulAziz University（阿卜杜勒阿齐兹国王大学）；Clemson University（克莱姆森大学）；International Computer Science Institute（国际计算机科学研究所）
"These cameras are just like the Eye of Sauron": A Sociotechnical Threat Model for AI-Driven Smart Home Devices as Perceived by UK-Based Domestic Workers
King’s College London（伦敦国王学院）；University of St Andrews（圣安德鲁斯大学）；Universitat Politècnica de València（瓦伦西亚理工大学）；INGENIO (CSIC-UPV)（INGENIO（西班牙国家研究委员会-瓦伦西亚理工大学））
