基于人机隐私共有的多维度个体隐私保护和多主体隐私保护机制研究

Research on Multi-Dimensional Individual Privacy Protection and Multi-Subject Privacy Protection Mechanisms Based on Human Computer Privacy Shared

摘要: 在智能算法时代，企业通过自动化数据采集与算法推荐服务为用户带来便利的同时，也引发了用户对个体隐私保护的担忧。本研究基于扎根理论，通过对10名被访者的深度访谈，探究了人机交互视角下用户隐私保护行为的影响因素、多维度个体隐私保护及多主体隐私保护机制。研究发现，用户隐私保护行为主要受数据来源特征、平台因素、推荐内容相关性以及用户感知的影响，并表现为信息隐藏、痕迹清除等隐私保护行为。研究构建了多主体协同的隐私保护模型，提出平台应优化隐私设置、政府需加强监管、用户需提升隐私素养，以实现算法服务与隐私保护的平衡，为构建健康的数字生态提供理论参考与实践启示。

Abstract: In the era of intelligent algorithms, while enterprises bring convenience to users through automated data collection and algorithm recommendation services, it also raises concerns among users about individual privacy protection. This study is based on grounded theory and explores the influencing factors of privacy protection behavior, multidimensional individual privacy protection, and multi-agent privacy protection mechanisms from the perspective of human-computer interaction through in-depth interviews with 10 respondents. Research has found that user privacy protection behavior is mainly influenced by Data Source Characteristics, Platform Factors, Relevance of Recommended Content, and User Perception, and is manifested as protective behaviors such as Information Hiding and Trace Removal. A multi-agent collaborative privacy protection model has been developed, proposing that platforms should optimize privacy settings, governments need to strengthen supervision, and users need to improve their privacy literacy to achieve a balance between algorithm services and privacy protection, providing theoretical reference and practical inspiration for building a healthy digital ecosystem.