论文标题
通过随机响应的私人机器学习
Private Machine Learning via Randomised Response
论文作者
论文摘要
我们基于随机响应引入了一个通用的私人机器学习框架。我们的假设是所有参与者都是潜在的对抗性，因此我们相信只能发布个人数据标记的单个嘈杂版本。我们讨论了一种一般方法，该方法构成了一种一致的方法来估计真正的基础机器学习模型，并在逻辑回归的情况下证明了这一点。
We introduce a general learning framework for private machine learning based on randomised response. Our assumption is that all actors are potentially adversarial and as such we trust only to release a single noisy version of an individual's datapoint. We discuss a general approach that forms a consistent way to estimate the true underlying machine learning model and demonstrate this in the case of logistic regression.
