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 …

Local Differential Privacy (LDP) provides provable privacy protection for data collection
without the assumption of the trusted data server. In the real-world scenario, different data …

In local differential privacy (LDP), each user perturbs her data locally before sending the
noisy data to a data collector. The latter then analyzes the data to obtain useful statistics …

Local differential privacy (LDP), where users randomly perturb their inputs to provide
plausible deniability of their data without the need for a trusted party, has been adopted …

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 …

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 …

Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …

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 …

Local differential privacy (LDP) is a well-established privacy protection scheme for collecting
sensitive data, which has been integrated into major platforms such as iOS, Chrome, and …

As massive data are produced from small gadgets, federated learning on mobile devices
has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) …