Data collection under local differential privacy (LDP) has been mostly studied for
homogeneous data. Real-world applications often involve a mixture of different data types …

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 …

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) 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), 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 …

Local Differential Privacy (LDP) is popularly used in practice for privacy-preserving data
collection. Although existing LDP protocols offer high utility for large user populations …

A key factor in big data analytics and artificial intelligence is the collection of user data from a
large population. However, the collection of user data comes at the price of privacy risks, not …

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 …

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 …

Releasing representative data sets without compromising the data privacy has attracted
increasing attention from the database community in recent years. Differential privacy is an …