协作过推系统中的差异性隐私:一篇回顾
Peter Müllner1,2, Elisabeth Lex2, Markus Schedl3,4
1Know-Center Gmbh, Graz, Austria.
Frontiers in big data
|October 30, 2023
概括
推系统会给用户隐私带来风险. 不同隐私保护数据,但降低了推质量. 本综述探讨了在这些系统中平衡隐私和准确性的方法.
科学领域:
- 计算机科学 计算机科学
- 信息检索 信息检索
- 网络安全 网络安全
背景情况:
- 推系统提供个性化的内容,但由于使用用户数据,引发了隐私问题.
- 差异隐私是一种通过引入噪音来保护用户数据的常见技术,但它通常会降低推准确度.
- 在维护用户隐私和确保高质量的建议之间存在着重要的权衡.
研究的目的:
- 提供推系统中隐私威胁的全面概述.
- 引入差异性隐私框架来保护用户数据.
- 审查和突出研究,以提高隐私和推系统的准确性之间的平衡.
主要方法:
- 推系统中的隐私漏洞概述.
- 介绍不同隐私的原则和应用.
- 对现有的推者系统方法进行系统审查,采用差异隐私.
- 研究分析的重点是减轻准确性-隐私权的权衡.
主要成果:
- 确定了推系统架构中固有的关键隐私威胁.
- 详细介绍了可以应用差异隐私来保护用户数据的机制.
- 列出了各种技术,旨在在不同的隐私约束下提高推质量.
- 突出了优化隐私-实用平衡的研究方向.
结论:
- 差异隐私是推系统中隐私的可行,尽管不完美的解决方案.
- 持续的研究对于改进方法至关重要,以改善准确性-隐私权的权衡.
- 未来的工作应该解决复杂的问题,如隐私-公平关系和各种用户隐私需求.
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