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 …

Organizations with a large user base, such as Samsung and Google, can potentially benefit
from collecting and mining users' data. However, doing so raises privacy concerns, and risks …

Consider statistical learning (eg discrete distribution estimation) with local $\epsilon $-
differential privacy, which preserves each data provider's privacy locally, we aim to optimize …

With the deep penetration of the Internet and mobile devices, privacy preservation in the
local setting has become increasingly relevant. The local setting refers to the scenario where …

The categorical data that have natural ordering between categories are termed ordinal data,
which are pervasive in numerous areas, including discrete sensor readings, metering data …

Abstract Local Differential Privacy (LDP) can provide each user with strong privacy
guarantees under untrusted data curators while ensuring accurate statistics derived from …

When collecting information, local differential privacy (LDP) relieves the concern of privacy
leakage from users' perspective, as user's private information is randomized before sent to …

We study discrete distribution estimation under user-level local differential privacy (LDP). In
user-level $\varepsilon $-LDP, each user has a $ m\ge1 $ samples and the privacy of all $ m …

Local differential privacy (LDP), where each user perturbs her data locally before sending to
an untrusted data collector, is a new and promising technique for privacy-preserving …

With local differential privacy (LDP), users can privatize their data and thus guarantee
privacy properties before transmitting it to the server (aka the aggregator). One primary …