公平的差异性隐私权 公平的差异性隐私权
Vasundhara Kaul1, Tamalika Mukherjee2
1Department of Sociology, Purdue University, West Lafayette, IN, United States.
Frontiers in big data
|September 2, 2024
概括
公平的差异隐私 (DP) 需要包容性沟通以及算法调整,以确保所有群体都能公平地代表数据. 这种方法对于防止隐私措施加剧社会不平等,特别是对边缘化人口而言至关重要.
科学领域:
- 计算机科学 计算机科学
- 社会科学 社会科学 社会科学
- 公共政策 公共政策
背景情况:
- 不同隐私 (DP) 增强了数据保密性,但引起了对准确性的担忧,特别是在边缘化群体.
- 不准确的DP数据可能会导致加剧社会不平等的政策,需要公平的隐私解决方案.
- 现有的研究重点是算法公平性,但在公平的DP中沟通的作用未得到充分探索.
研究的目的:
- 引入和概念化公平的DP,强调整个部署生命周期的包容性沟通.
- 分析美国人口普查局对2020年人口普查DP系统的沟通策略,使用包容性科学沟通 (ISC) 框架.
- 为政府组织提供关于实施公平DP的可操作教训.
主要方法:
- 公平DP的概念化,整合算法和沟通策略.
- 对2020年美国人口普查避免披露系统采用的案例研究分析.
- 应用包容性科学传播 (ISC) 框架来评估沟通实践.
主要成果:
- 2020年美国人口普查案例研究显示,沟通策略未能充分吸引多样化的数据用户.
- 有效的沟通对于减轻DP引起的数据扭曲和确保公平的结果至关重要.
- 没有包容性沟通,单单算法股权参数是不够的.
结论:
- 公平的DP需要一个整体的方法,包括算法设计,包容性沟通和实施.
- 政府组织必须优先考虑包容性沟通,以确保DP系统公平地惠及所有社会群体.
- 如果不能确保公平的DP,可能会延续或加剧社会不平等,破坏政策目标.
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