The widespread use of mobile devices such as smartphones, tablets, and smartwatches has
led users to constantly generate various location data during their daily activities …

Local differential privacy (LDP) is a recently proposed privacy standard for collecting and
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …

We consider the minimax estimation problem of a discrete distribution with support size k
under privacy constraints. A privatization scheme is applied to each raw sample …

LDP (Local Differential Privacy) has been widely studied to estimate statistics of personal
data (eg, distribution underlying the data) while protecting users' privacy. Although LDP …

For the purpose of improving the quality of services, softwares or online services are
collecting various of user data, such as personal information and locations. Such data …

We study the fundamental problems of identity testing (goodness of fit), and closeness
testing (two sample test) of distributions over $ k $ elements, under differential privacy. While …

The local model for differential privacy is emerging as the reference model for practical
applications of collecting and sharing sensitive information while satisfying strong privacy …

A number of studies have recently been made on discrete distribution estimation in the local
model, in which users obfuscate their personal data (eg, location, response in a survey) by …

We consider a setup in which confidential iid samples X 1,..., Xn from an unknown finite-
support distribution p are passed through n copies of a discrete privatization channel (aka …

In machine learning, boosting is one of the most popular methods that is designed to
combine multiple base learners into a superior one. The well-known Boosted Decision Tree …