校准的可选随机响应技术,以高效和可靠地估计量化敏感变量
Ahmed Audu1,2, Mojeed Abiodun Yunusa3, Maggie Aphane4
1Department of Maths & App. Maths, Sefako Makgatho Health Sciences University, Pretoria, South Africa. ahmed.audu@udusok.edu.ng.
Scientific reports
|May 17, 2025
概括
新的校准随机响应技术 (C-ORRT) 模型改善了敏感数据估计. 与现有方法相比,这些C-ORRT模型提供了更高的效率,稳定性和隐私性,在真实和模拟数据应用中表现出卓越的性能.
科学领域:
- 统计 统计 统计 统计
- 调查方法 调查方法
- 数据 隐私 数据 隐私 数据
背景情况:
- 准确估计敏感信息是一项挑战.
- 现有的随机响应技术 (RRT) 在效率和隐私方面存在局限性.
- 辅助变量可以潜在地改善RRT模型.
研究的目的:
- 引入新的校准随机响应技术 (C-ORRT) 模型.
- 提高RRT模型的效率,稳定性和稳定性.
- 提高敏感信息的估计,同时保持隐私.
主要方法:
- 使用辅助变量校准修改现有的RRT模型.
- 导出理论属性,包括估计器,差异和隐私级别.
- 开发了一种用于效率和隐私评估的综合指标.
主要成果:
- 在C-ORRT模型中,偏差较小,差异较小.
- 实现了更高的相对效率和增强的隐私水平.
- 在差异和隐私的综合指标中展示了卓越的性能.
结论:
- 拟议的C-ORRT模型比现有的RRT模型更高效,更稳定,更强大.
- C-ORRT模型为敏感数据估计提供了一种优越的方法.
- 数字应用验证了C-ORRT模型的理论优势.
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