集体隐私恢复:通过分散的人工智能协调数据共享
Evangelos Pournaras1, Mark Christopher Ballandies2, Stefano Bennati2
1School of Computing, University of Leeds, Leeds LS2 3JT, UK.
PNAS nexus
|February 8, 2024
概括
协调数据共享为在线服务提供了重要的隐私恢复和成本降低. 这种方法将个人数据视为稀缺资源,有利于个人和提供商.
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
- 数据 隐私 数据 隐私 数据
背景情况:
- 集体隐私丧失对个人自由和民主原则构成重大威胁.
- 当前的数据共享实践往往无法平衡服务需求和个人隐私.
- 数据作为稀缺资源的概念需要新的共享范式.
研究的目的:
- 通过协调的数据共享来调查重大隐私恢复的潜力.
- 使用分散的人工智能自动化和扩展复杂的集体数据共享安排.
- 在现实的环境中比较不同的数据共享模型 (态度,内在,奖励,协调).
主要方法:
- 一个严格的实验室实验,涉及真实数据披露.
- 应用因果推断和集群分析技术.
- 对四种不同的数据共享行为进行比较.
主要成果:
- 协调数据共享已成为恢复隐私的高效战略.
- 服务提供商的成本大幅降低.
- 确定了预测隐私和不同数据共享行为的关键标准.
结论:
- 协调数据共享是一个"双赢"的场景,增强个人隐私,同时降低服务提供商的成本.
- 分散的人工智能可以自动化和扩展保护隐私的数据共享机制.
- 这些发现支持将个人数据视为通过集体协调管理的稀缺资源.
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