  In machine learning, boosting is one of the most popular methods that
designed to combine multiple base learners to a superior one. The well-known
Boosted Decision Tree classifier, has been widely adopted in many areas. In the
big data era, the data held by individual and entities, like personal images,
browsing history and census information, are more likely to contain sensitive
information. The privacy concern raises when such data leaves the hand of the
owners and be further explored or mined. Such privacy issue demands that the
machine learning algorithm should be privacy aware. Recently, Local
Differential Privacy is proposed as an effective privacy protection approach,
which offers a strong guarantee to the data owners, as the data is perturbed
before any further usage, and the true values never leave the hands of the
owners. Thus the machine learning algorithm with the private data instances is
of great value and importance. In this paper, we are interested in developing
the privacy-preserving boosting algorithm that a data user is allowed to build
a classifier without knowing or deriving the exact value of each data samples.
Our experiments demonstrate the effectiveness of the proposed boosting
algorithm and the high utility of the learned classifiers.
