Local Differential Privacy (LDP) protocols enable an untrusted data collector to perform
privacy-preserving data analytics. In particular, each user locally perturbs its data to …

Local Differential Privacy (LDP) protocols enable an untrusted server to perform privacy-
preserving, federated data analytics. Various LDP protocols have been developed for …

Although local differential privacy (LDP) protects individual users' data from inference by an
untrusted data curator, recent studies show that an attacker can launch a data poisoning …

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 …

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 …

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 …

Differential privacy (DP) is currently the de-facto standard for achieving privacy in data
analysis, which is typically implemented either in the" central" or" local" model. The local …

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 is a widely studied restriction on distributed algorithms that collect
aggregates about sensitive user data, and is now deployed in several large systems. We …

Computing aggregate statistics about user data is of vital importance for a variety of services
and systems, but this practice has been shown to seriously undermine the privacy of users …