对低保真度合成数据进行四次检查:对披露控制和质量评估的建议
Gillian M Raab1, Sophie McCall2, Liam Cavin3
1Scottish Centre for Administrative Data Research, Futures Institute, 1 Lauriston Pl, Edinburgh, EH3 9EF.
International journal of population data science
|November 20, 2025
概括
低保真性合成数据 (LFSD) 可以帮助在可信研究环境 (TREs) 外的研究人员进行代码测试. 清晰的文档和披露风险评估对于LFSD实用性和防止数据泄露至关重要.
科学领域:
- 数据科学数据科学数据科学
- 信息安全 信息安全
- 研究方法研究方法研究方法学
背景情况:
- 机密的行政数据通常仅限于信任研究环境 (TREs) 的研究人员.
- 建议使用低保真合成数据 (LFSD) 进行外部使用,使代码测试和数据发现成为可能.
- 关于LFSD创建和特征的透明度对于用户的理解至关重要.
研究的目的:
- 概述LFSD释放的基本质量标准和透明度措施.
- 确保LFSD在保持数据安全的同时,对于初步分析是有用的.
- 引导数据控制者在负责地准备和发布LFSD时提供指导.
主要方法:
- 建议对LFSD释放进行检查,包括明确标记为合成数据.
- 强制披露LFSD的风险评估和缓解策略.
- 强调保持LFSD和TRE数据之间的结构相似性,有记录的差异.
主要成果:
- LFSD发布需要仔细考虑标签,披露风险,数据结构和全面的文档.
- 变量关系在LFSD中没有保留,这可能会带来披露风险.
- 与TRE数据相比,对LFSD的局限性进行明确的沟通对于准确的初步分析是必要的.
结论:
- 数据控制者必须在发布之前进行检查,以确保LFSD的文件化,有用性和保密性.
- 建议对LFSD发布规则采取灵活的,背景意识的方法,而不是严格的,基于规则的方法.
- 有效的LFSD管理平衡了数据可访问性与强有力的隐私保护.
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