Private boosted decision trees via smooth re-weighting

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2021-08-06
Authors/Contributors
Abstract
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 sparsity and accuracy than other differentially private ensemble classifiers.
Document
Extent
39 pages.
Identifier
etd21611
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Shinkar, Igor
Language
English
Member of collection
Attachment Size
etd21611.pdf 460.91 KB