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实时流数据匿名化通过动态重新配置与多样性增强 SUHDSA
1Department of AI Information Security, Halla University, Wonju 26404, Republic of Korea.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究引入了一种新的实时数据流匿名化方法,通过结合k-匿名性和l-多样性来增强隐私. 该方法有效地减少了信息丢失,并在严格的延迟约束下改善了隐私指标.
科学领域:
- 数据隐私和安全数据隐私和安全
- 信息系统信息系统信息系统
- 计算机科学 计算机科学
背景情况:
- 传统的k-匿名性是不够的保护对属性披露与倾斜的敏感属性.
- 高通量数据流需要实时匿名化技术,遵守严格的延迟预算 (β).
研究的目的:
- 开发和评估一个实时匿名化策略,共同执行数据流的k-匿名性和l-多样性.
- 尽量减少信息丢失和扭曲,同时在延迟约束下保持隐私保证.
主要方法:
- 实现了一个延迟感知监控触发修复控制器,用于实时匿名化.
- 使用加权目标函数 (λΔIL + (1 - λ) ΔRT) 来平衡信息丢失 (IL) 和实时约束 (RT).
- 采用了顶部边界机制 (邻近顶部"c"和增长顶部"γ") 并评估了一个自适应控制器.
主要成果:
- 确定了增强隐私 (k-匿名性和 l-多样性) 不会显著增加数据扭曲的运营区域,特别是中等至高k和足够的延迟预算 (β).
- 证明了隐私指标的改进,包括l-满意度和.
- 展示了像λ,c和γ这样的参数在延迟和扭曲之间提供可解释的权衡,抑制重新配置和尾部延迟.
结论:
- 在实时数据流中联合执行k-匿名性和l-多样性是可行的,并且可以单独超过k-匿名性.
- 拟议的控制器有效平衡隐私,扭曲和延迟,为高通量数据匿名化提供实用解决方案.
- 参数调整为基于特定应用程序要求优化匿名化策略提供了灵活性.
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