Private Boosted Decision Trees via Smooth Re-Weighting
This work addresses privacy concerns for individuals whose data is used in machine learning, though it is incremental as it builds on existing differential privacy and boosting techniques.
The paper tackles the problem of training boosted decision trees with differential privacy guarantees, resulting in an algorithm that achieves better model sparsity and accuracy compared to other differentially private ensemble classifiers.
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.