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 …

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 …

Local differential privacy (LPD) is a distributed variant of differential privacy (DP) in which the
obfuscation of the sensitive information is done at the level of the individual records, and in …

Local differential privacy (LDP), which perturbs each user's data locally and only sends the
noisy version of her information to the aggregator, is a popular privacy-preserving data …

Location statistics collective release provides essential information to understand crucial
phenomena, including points of interest and movement patterns. Sharing location statistics …

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 …

Differential privacy has recently emerged as the de facto standard for private data release.
This makes it possible to provide strong theoretical guarantees on the privacy and utility of …

In this article, we propose local information privacy (LIP), and design LIP based mechanisms
for statistical aggregation while protecting users' privacy without relying on a trusted third …

We study the problem of clustering sensitive data while preserving the privacy of individuals
represented in the dataset, which has broad applications in practical machine learning and …

Recently, local differential privacy (LDP), as a strong and practical notion, has been applied
to deal with privacy issues in data collection. However, existing LDP-based strategies mainly …