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潘达游戏:基于游戏理论模型的个人级流行病数据的优化隐私保护发布
IEEE transactions on nanobioscience
|June 8, 2023
概括
一个新的游戏理论模型优化了COVID-19数据共享,平衡了隐私和实用性. 这种适应性方法的性能优于当前的方法,在不损害个人隐私的情况下,确保有价值的公共卫生见解.
科学领域:
- 计算流行病学计算流行病学
- 数据隐私 数据隐私
- 游戏理论的游戏理论.
背景情况:
- 分享个人级别的流行病数据,如COVID-19,对于疾病理解和公共卫生监测至关重要.
- 目前用于COVID-19数据的非识别方法可能无法适应不断变化的感染率,从而造成隐私风险或损害数据实用性.
- 优化数据隐私和实用性之间的平衡对于有效的公共卫生研究至关重要.
研究的目的:
- 在发布个人级别的COVID-19数据时引入适应性政策生成的游戏理论模型.
- 根据感染动态,优化隐私风险和数据实用性之间的权衡.
- 提高COVID-19数据的可用性,用于研究和公共卫生监测.
主要方法:
- 模拟数据发布作为数据发布者和接收者之间的双人Stackelberg游戏.
- 结合了未来病例数量的预测和原始和发布数据之间的相互信息.
- 利用了来自范德比尔特大学医学中心的COVID-19病例数据 (2020年3月至2021年12月).
主要成果:
- 与最先进的基线 (包括CDC方法) 相比,游戏理论模型表现出更高的性能.
- 该模型有效地保持了低隐私风险,同时提高了数据实用性.
- 灵敏度分析证实了参数变化中发现的稳定性.
结论:
- 拟议的游戏理论模型为发布个人级别的COVID-19数据提供了一个有效的策略.
- 这种适应性方法成功地平衡了隐私问题与公共卫生中可用的数据的需求.
- 该模型在现有的传染病数据共享政策上取得了重大进展.
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