摘 要
随着大数据和人工智能技术的快速发展，智能推荐系统已广泛应用于电子商务、社交媒体、新闻资讯等领域，极大地提升了用户体验与平台运营效率。然而，推荐系统在收集和处理用户数据的过程中也带来了严重的隐私泄露风险，引发了公众和监管机构的高度关注。因此，如何在提升推荐效果的同时有效保护用户隐私，成为当前研究的重要课题。本研究旨在深入探讨智能推荐系统中的用户隐私保护机制，提出具有可行性和创新性的解决方案。本文通过文献分析、算法设计与实验验证相结合的方法，系统梳理了现有推荐系统中存在的隐私问题与应对策略，并在此基础上提出了一种基于差分隐私与联邦学习的混合隐私保护框架。该框架在保障用户数据本地化存储的前提下，结合加密计算与可信执行环境，实现了高效推荐与隐私保护的平衡。实验结果表明，所提方法在多个推荐性能指标上接近或优于传统非隐私保护模型，同时显著提升了数据安全性。本研究的主要贡献在于构建了一个兼顾实用性与安全性的隐私保护架构，并为未来推荐系统的合规发展提供了理论支持与实践指导。
关键词：智能推荐系统；隐私保护；差分隐私；联邦学习；数据安全
Abstract
With the rapid development of big data and artificial intelligence technologies, intelligent recommendation systems have been widely applied in fields such as e-commerce, social media, and news platforms, significantly enhancing user experience and operational efficiency. However, these systems also pose serious risks of privacy leakage during the collection and processing of user data, drawing increasing attention from both the public and regulatory authorities. Therefore, how to effectively protect user privacy while improving recommendation performance has become a critical research topic. This study aims to thoroughly investigate user privacy protection mechanisms within intelligent recommendation systems and proposes feasible and innovative solutions. By integrating literature analysis, algorithm design, and experimental validation, this research systematically reviews existing privacy issues and countermeasures in recommendation systems, and on this basis, develops a hybrid privacy-preserving fr amework combining differential privacy and federated learning. The proposed fr amework ensures localized storage of user data, and by incorporating encrypted computation and trusted execution environments, achieves a balance between efficient recommendation and privacy protection. Experimental results demonstrate that the proposed method performs competitively with or better than traditional non-privacy-preserving models across multiple recommendation metrics, while significantly enhancing data security. The primary contributions of this study lie in constructing a privacy-preserving architecture that balances practicality and security, and providing theoretical support and practical guidance for the compliant development of future recommendation systems.
Keywords: Intelligent Recommendation System; Privacy Protection; Differential Privacy; Federated Learning; Data Security
目 录
1绪论 1
1.1研究《智能推荐系统中的用户隐私保护机制研究》的背景和意义 1
1.2《智能推荐系统中的用户隐私保护机制研究》领域的研究现状 1
1.3本文研究《智能推荐系统中的用户隐私保护机制研究》的方法 2
2智能推荐系统中用户隐私的内涵与风险分析 2
2.1用户隐私在智能推荐系统中的定义与分类 2
2.2推荐系统数据采集过程中的隐私泄露风险 2
2.3数据处理阶段的隐私安全隐患 3
2.4隐私风险对用户信任的影响机制 3
3现有隐私保护机制在推荐系统中的应用评估 4
3.1差分隐私技术在推荐算法中的实现方式 4
3.2加密与安全多方计算的应用效果分析 4
3.3匿名化与去标识化方法的适用性探讨 5
3.4基于联邦学习的隐私保护机制实践评价 5
4面向推荐系统的动态隐私保护机制设计 5
4.1动态隐私保护机制的设计原则与目标 6
4.2基于用户偏好的个性化隐私策略生成 6
4.3多粒度隐私控制模型构建 6
4.4隐私保护与推荐精度之间的平衡机制 7
5隐私保护机制的实施路径与政策建议 7
5.1技术层面：推荐系统架构的隐私增强设计 7
5.2制度层面：隐私保护合规性框架建设 8
5.3用户层面：隐私意识提升与授权管理优化 8
5.4行业协同：跨平台隐私保护标准制定 9
结 论 9
参考文献 10
致 谢 11
