论文标题
通过平稳加权的私人提升决策树
Private Boosted Decision Trees via Smooth Re-Weighting
论文作者
论文摘要
保护机器学习算法使用数据的人的隐私很重要。差异隐私是正式保证隐私保证的适当数学框架，而提升的决策树是一种流行的机器学习技术。因此，我们建议并测试一种实用算法，以促进保证差异隐私的决策树。隐私是因为我们的助推器从未在任何一个例子上施加过多的重量。这样可以确保每个人的数据永远不会影响一棵树“太多”。实验表明，这种增强算法比其他差异私人集合分类器可以产生更好的模型稀疏性和准确性。
Protecting the privacy of people whose data is used by machine learning algorithms is important. Differential Privacy is the appropriate mathematical framework for formal guarantees of privacy, and boosted decision trees are a popular machine learning technique. So we propose and test a practical algorithm for boosting decision trees that guarantees differential privacy. Privacy is enforced because our booster never puts too much weight on any one example; this ensures that each individual's data never influences a single tree "too much." Experiments show that this boosting algorithm can produce better model sparsity and accuracy than other differentially private ensemble classifiers.
