对差异性隐私能力的定量和定性评估,以支持合作研究,使用现实世界的数据分析分析
David P Gieser1, Ashna R Arya1, Rebecca Lee Smith2,3
1The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States.
JAMIA open
|November 11, 2025
概括
差异隐私 (DP) 可以在协作研究中保护患者数据. 使用DP聚合数据减轻了准确性损失,显示了在COVID-19大流行期间分析癌症诊断的前景.
科学领域:
- 医疗信息学 医疗信息学
- 医疗数据 隐私 医疗数据 隐私
- 流行病学 流行病学
背景情况:
- 在保持隐私的同时,为合作研究共享临床数据是具有挑战性的.
- 不同隐私 (DP) 提供了一种方法来保护敏感信息,通过向数据添加噪音.
- 在分析现实世界健康数据 (如癌症诊断) 中,DP的实用性需要进一步评估.
研究的目的:
- 评估差异化私有数据的实用性,以分析COVID-19和新癌症诊断 (NCC) 之间的关联.
- 评估不同聚合水平 (1周与4周) 如何影响DP分析中的数据实用性.
- 探索在医疗保健研究中对DP平台的潜在改进.
主要方法:
- 从多医院系统中提取成人癌症诊断数据 (2019-2021年).
- 查询原始和差异性私密 (DP) 数据集,以1周和4周的间隔汇总.
- 进行了回归分析,以检查COVID-19率与NCC变化之间的关联.
主要成果:
- 新癌症诊断 (NCC) 在2020年下降,并在2021年反弹,这一模式反映在4周的DP数据集中,但不是1周的DP数据集.
- 与原始数据相比,DP分析产生了更广泛的置信区间,4周的聚合显示了比1周更窄的间隔.
- 在原始数据中发现的显著关联在DP数据集中没有被检测到,这表明隐私和分析准确性之间的权衡.
结论:
- 不同隐私降低了分析准确性,但聚合可以减轻这种权衡.
- 可以增强DP平台,以更好地支持保护隐私,协作医疗保健研究.
- 进一步开发DP工具对于实现强大的跨机构医学研究至关重要.
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