CURATE:扩展差异化私人因果图发现
Payel Bhattacharjee1, Ravi Tandon1
1Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721, USA.
Entropy (Basel, Switzerland)
|November 27, 2024
概括
CURATE是一种用于差别私密因果图发现的新框架,可以自适应地预算隐私. 这种方法提高了预测准确度,并减少了与现有方法相比隐私泄露.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 统计 统计 统计 统计
背景情况:
- 因果图发现 (CGD) 从数据中估计概率图形模型.
- 差异隐私 (DP) 对于保护CGD.中的敏感观察数据至关重要.
- 现有的DP-CGD方法采用统一的噪声,影响算法性能.
研究的目的:
- 引入CURATE,一个具有适应性隐私预算的差异性私有因果图发现框架.
- 为解决DP-CGD连续工艺中均噪声造成的性能下降问题.
- 为了提高实用性,并尽量减少DP-CGD的隐私泄露.
主要方法:
- 开发了CURATE,这是一个DP-CGD框架,具有适应性隐私预算.
- 实施了自适应预算,以尽量减少基于约束的CGD错误概率.
- 在基于分数的CGD中,优化代计数,同时限制累积隐私泄露.
主要成果:
- 与现有的DP-CGD算法相比,CURATE显示出更高的实用性.
- 该框架实现了减少隐私泄露.
- 在多个数据集上的实验验证证证了CURATE的有效性.
结论:
- 在因果图发现中,CURATE提供了一种有效的方法来平衡隐私和实用性.
- 适应性隐私预算是提高DP-CGD性能的关键.
- 该框架为估计因果关系提供了更准确和保护隐私的方法.
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