Local differential privacy (LDP) has been deemed as the de facto measure for privacy-
preserving distributed data collection and analysis. Recently, researchers have extended …

Key-value data is a naturally occurring data type that has not been thoroughly investigated
in the local trust model. Existing local differentially private (LDP) solutions for computing …

With the advanced technologies for ubiquitous computing, the large sensitive big data is
valuable to explore the associations of data for epidemiology. New data privacy regulations …

With the increasing number of intelligent devices joining into the Internet of Things (IoT),
traditional centralized learning struggles to meet the performance requirements of terminal …

Local Differential Privacy (LDP), where each user perturbs her data locally before sending to
an untrusted party, is a new and promising privacy-preserving model for mobile data …

Local differential privacy (LDP) is a promising technique to realize privacy-preserving data
aggregation without a trusted aggregator. Normally, an LDP protocol requires each user to …

Data aggregation in the setting of local differential privacy (LDP) guarantees strong privacy
by providing plausible deniability of sensitive data. Existing works on this issue mostly …

Numerical vector aggregation has numerous applications in privacy-sensitive scenarios,
such as distributed gradient estimation in federated learning, and statistical analysis on key …

Local differential privacy (LDP) can provide each user with strong privacy guarantees under
untrusted data curators while ensuring accurate statistics derived from privatized data. Due …

The protocols that satisfy Local Differential Privacy (LDP) enable untrusted third parties to
collect aggregate information about a population without disclosing each user's privacy. In …