对合成纵向健康数据的隐私风险评估
Julian Schneider1, Marvin Walter1, Karen Otte2
1Knowledge Management, ZB MED - Information Centre for Life Sciences, Cologne, Germany.
Studies in health technology and informatics
|September 5, 2024
概括
评估合成数据的隐私风险是一项挑战. 匿名测量器框架评估了一项流行病学研究中的漏洞,揭示了不同的隐私得分,并强调了对合成数据集更好的隐私风险评估方法的需要.
科学领域:
- 数据隐私 数据隐私
- 合成数据生成的合成数据生成.
- 流行病学 流行病学
背景情况:
- 合成数据生成方法为数据隐私提供了一种现代化的方法,通常声称与传统匿名化相比,优越的公用事业隐私权权权衡.
- 深度学习模型可以生成有用的合成数据集,但评估它们的隐私影响,特别是关于数据保护准则,仍然很困难.
研究的目的:
- 评估为流行病学研究生成的合成数据的隐私影响.
- 评估匿名测量器框架在量化合成数据集中的隐私风险方面的有效性.
- 识别合成数据中的特定漏洞,特别是与异常值有关的漏洞.
主要方法:
- 应用匿名隐私风险量化框架的应用.
- 从DONALD队列研究 (1312名参与者,16个时间点) 中生成的合成数据的分析.
- 关注隐私风险,包括挑选,可链接性和属性推断,重点关注异常漏洞.
主要成果:
- 在不同类型的攻击中,隐私得分在不同类型的攻击中差异很大.
- 该研究确定了与合成数据中的异常值相关的特定漏洞.
结论:
- 对合成数据的隐私风险评估是一个持续的挑战.
- 隐私风险评估结果的实施和解释存在困难.
- 需要进一步的研究来开发合成数据隐私风险评估的强大方法.
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