对于分类器评估的差异性隐私
Kendrick Boyd1, Eric Lantz1, David Page2
1Department of Computer Sciences, University of Wisconsin-Madison.
概括
不同隐私保护敏感数据. 本研究引入了用于准确计算机器学习模型性能指标的新方法,例如ROC曲线面积,同时保持隐私保证.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 保护隐私的技术 保护隐私的技术
背景情况:
- 不同隐私 (DP) 提供了强有力的数据保护保障.
- 现有的DP方法主要侧重于模型培训,而不是绩效评估.
- 报告ML模型性能指标可以无意中披露私人信息.
研究的目的:
- 开发用于计算ML评估指标的差异化私有机制.
- 为了解决与报告模型性能相关的隐私风险.
- 为了实现机器学习模型的安全和私人评估.
主要方法:
- 研究了用于计算关键绩效指标的差异性私有算法.
- 专注于诸如接收器运行特征 (ROC) 曲线下的面积等指标.
- 开发和分析了新的隐私保护计算技术.
主要成果:
- 成功确定了用于ROC AUC计算的有效差异性私有机制.
- 证明了计算平均精度与差异隐私的可行性.
- 量化了拟议方法的隐私-实用性权衡.
结论:
- 对ML评估指标的差异性私有计算是可以实现和必要的.
- 开发的方法允许对模型性能进行私人报告.
- 这项工作推进了保护隐私的机器学习评估领域.
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