半局部 临床数据的时间敏感匿名化
Freimut Gebhard Herbert Hammer1, Mateusz Buglowski1, André Stollenwerk2
1RWTH Aachen University, Informatik 11 - Embedded Software, 52056, Aachen, Germany.
Scientific data
|December 20, 2024
概括
这项研究引入了一种用于匿名化时间连续数据的新方法,保留时间和价值关系,同时确保k-匿名性和t-密闭性免受数据攻击.
科学领域:
- 数据隐私和安全数据隐私和安全
- 时间序列分析分析时间序列分析
- 信息的匿名化信息的匿名化.
背景情况:
- 保护敏感的时间连续数据免受链接和分发攻击至关重要.
- 现有的匿名化方法可能无法充分保护数据的时间和价值维度.
- 确保强大的隐私保证,如k-匿名性和t-接近性,对于数据实用性至关重要.
研究的目的:
- 提出一种用于匿名化时间连续数据的新方法.
- 为了保持时间和价值维度之间的关系.
- 提供强有力的隐私保证,防止链接和分发攻击.
主要方法:
- 具有窗口的Fréchet分割用于时间轴细分,以最大限度地减少信息丢失.
- 分布 聚类用于生成代表性人口分布.
- 使用含有隐性成本和t-closeness的Fréchet距离进行桶化.
- 多重重新分配阶段和半局部决策,以实现运行时间优化.
主要成果:
- 实现了较低的信息丢失和中位数相对误差.
- 证明了有效的t-closeness用于隐私保护.
- 通过半本地决策,显著减少运行时间.
结论:
- 拟议的方法有效地将时间连续数据匿名化,同时保持其完整性.
- 这种方法提供了对复杂数据攻击的强有力的保护.
- 该方法平衡了隐私保护与数据实用性和计算效率.
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