图书情报知识 ›› 2025, Vol. 42 ›› Issue (2): 70-82.doi: 10.13366/j.dik.2025.02.070

• 学术聚焦(2)· 不同场景下的隐私保护 • 上一篇    下一篇

智能家居使用场景的用户隐私风险感知与保护行为研究

曹雅宁1,2, 柯青1,2, 杨卉3   

  1. 1.南京大学数据智能与交叉创新实验室,南京,210023;
    2.南京大学信息管理学院,南京,210023;
    3.博西华电器(江苏)有限公司,南京,210046
  • 出版日期:2025-03-10 发布日期:2025-05-03
  • 通讯作者: 柯青(ORCID: 0000-0002-6300-2682),博士,教授,研究方向:人机交互与用户行为,Email: keqing@nju.edu.cn。
  • 作者简介:曹雅宁(ORCID: 0000-0001-9578-5987),博士研究生,研究方向:人机交互与用户行为,Email: caoyaning1@sina.com;杨卉(ORCID: 0000-0001-5395-6469),硕士,研究方向:人机交互与用户行为,Email: me1cury@sina.com。

Users' Privacy Risk Perception and Protective Behavior in Smart Home Usage Scene

CAO Yaning1,2, KE Qing1,2, YANG Hui3   

  1. 1. Laboratory of Data Intelligence and Interdisciplinary Innovation, Nanjing University, Nanjing, 210023;
    2. School of Information Management, Nanjing University, Nanjing, 210023;
    3. BSH Electrical Appliances(Jiangsu) Co., Ltd., Nanjing, 210046
  • Online:2025-03-10 Published:2025-05-03
  • Contact: Correspondence should be addressed to KE Qing, Email: keqing@nju.edu.cn, ORCID: 0000-0002-6300-2682

摘要: [目的/意义] 智能家居在提升家庭生活舒适度的同时,也引发了用户的隐私风险感知。探索智能家居用户的隐私风险感知与保护行为,有助于理解特殊情境下用户面临的隐私问题及用户行为模式,帮助提升智能家居的隐私保护设计。[研究设计/方法] 对37位用户进行半结构化访谈,采用程序化扎根理论分析访谈数据,围绕"智能家居用户的隐私风险感知"这一核心范畴构建理论模型,并描述了故事线。[结论/发现] 四类前因影响智能家居用户的隐私风险感知,包括隐私信息类型、隐私体验、外部保障评估及技术前景预判、其他信息产品经验;智能家居用户的隐私保护行为包括限制信息披露数量、管理产品使用场景和避免特定功能使用三类形态;智能家居用户的隐私风险感知与隐私保护行为存在相互影响关系,且感知价值调节了智能家居用户的隐私风险感知对隐私保护行为的影响;未发现智能家居使用场景下存在明显的隐私悖论现象。[创新/价值] 研究关注特定场景的用户隐私风险感知和保护行为,理论上促进了人智交互场景的用户隐私相关研究,实践上为智能家居的隐私保护设计提供参考,助力“人本人工智能”长期发展目标的实现。

关键词: 智能家居, 隐私, 隐私风险感知, 人智交互, 保护行为

Abstract: [Purpose/Significance] Smart homes have enhanced the comfort of home life, but they also raise privacy concerns for users. Exploring the users' privacy risk perception and protective behavior in the scene of smart home usage is helpful to understand the new privacy problems faced by users in special situations and their behavior patterns, and help enhance the privacy protection design of smart home. [Design/Methodology] Semi-structured interviews were conducted with 37 participants, Grounded Theory was employed to analyze the interview data, and a theoretical model was constructed around "privacy risk perception of smart home users", with the storyline being described. [Findings/Conclusion] It is found that four types of antecedents affect smart home users' perception of privacy risk, including the type of privacy information, privacy experience, external assurance assessment and technological prospects forecast, as well as other information products experience. In the smart home usage scene, users' privacy protective behaviors include three categories: limiting the amount of information disclosure, managing the usage scenarios and avoiding the use of specific functions. Privacy risk perception of smart home users and their privacy protective behavior interact with each other, and the perceived value moderates the impact of users' privacy risk perception of smart home on their privacy protective behavior. There is no clear indication of a privacy paradox in the scene of smart home usage. [Originality/Value] The research focuses on the users' privacy risk perception and protective behavior in specific scenarios, which theoretically promotes the user privacy related research in human-AI interaction scenarios, and practically provides a reference for the privacy protection design of smart homes, and helps realize the long-term development goal of "human-centered artificial intelligence".

Keywords: Smart home, Privacy, Privacy risk perception, Human-AI interaction, Protective behavior