医疗保险索赔的匿名化 药物安全性评估数据
Mehmed Halilovic1, Karen Otte1, Thierry Meurers1
1Medical Informatics Group, Berlin Institute of Health atCharité, Germany.
Studies in health technology and informatics
|September 3, 2025
概括
在医疗保险索赔数据中平衡隐私和实用性至关重要. 威胁建模比保守的匿名化提供了更好的方法,保留数据的实用性,同时确保可接受的研究隐私.
科学领域:
- 医疗信息学
- 数据隐私
- 医学研究
背景情况:
- 医疗保险索赔数据为患者护理提供了有价值的研究见解.
- 保护敏感的健康信息需要强有力的匿名化策略.
- 匿名化提供了隐私-实用性权衡,需要结构化的方法.
研究的目的:
- 系统地评估德国医疗保险索赔数据的匿名化策略.
- 为了比较保守和基于威胁建模的匿名化方法.
- 评估隐私保护与数据实用性之间的权衡.
主要方法:
- 使用ARX数据匿名化工具对1727条记录和45个变量进行了评估.
- 采用结构化的威胁建模方法与保守的策略.
- 使用匿名和会员推断攻击评估数据实用性和剩余隐私风险.
主要成果:
- 保守的匿名化提供了强大的隐私,但大大降低了数据的实用性.
- 基于威胁建模的匿名化保留了更高的数据实用性,在适度门下可接受的隐私.
- 具体的背景策略和经验风险评估至关重要.
结论:
- 在数据匿名化中比较隐私-实用性权衡的系统过程.
- 这些发现可以适应其他医疗数据集,指导匿名数据共享.
- 强调在医疗数据研究中需要特定环境的匿名化和经验风险评估.
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