你是一个异常者吗? 识别对健康数据集隐私攻击的目标
Mehmed Halilovic1, Thierry Meurers1, Karen Otte1
1Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Health Data Science, Charitéplatz 1, 10117 Berlin, Germany.
Studies in health technology and informatics
|August 23, 2024
概括
识别脆弱的健康记录是隐私攻击的关键. 不同的异常值检测方法产生不同的结果,最佳方法取决于具体的攻击策略.
科学领域:
- 计算机科学 计算机科学
- 数据 隐私 数据 隐私 数据
- 医疗信息学 医疗信息学
背景情况:
- 保护敏感的健康数据至关重要.
- 对健康记录的隐私攻击需要识别脆弱的记录 (目标).
- 异常值检测是识别潜在目标的常用方法.
研究的目的:
- 实施和评估三个不同的异常指标来检测脆弱的健康记录.
- 为了比较通过不同的异常值检测方法识别的top-k目标.
- 用合成数据评估这些已识别的目标对会员推断攻击的易感性.
主要方法:
- 实施和评估三个异常值检测指标.
- 通过不同的方法对top-k目标进行比较分析.
- 评估目标对合成数据的会员推断攻击的易感性.
主要成果:
- 没有单一的异常检测方法是普遍最佳的,用于识别脆弱的健康记录.
- 不同的方法可以识别不同的潜在目标.
- 目标选择的有效性取决于隐私攻击的性质.
结论:
- 选择目标选择方法应与正在考虑的特定隐私攻击保持一致.
- 为了有效保护健康数据的隐私,需要采用上下文依赖的方法.
- 进一步的研究可能会探索混合方法或适应性策略用于目标识别.
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