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

With the advent of the era of big data, privacy issues have been becoming a hot topic in
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …

The collection and analysis of telemetry data from user's devices is routinely performed by
many software companies. Telemetry collection leads to improved user experience but …

Protocols satisfying Local Differential Privacy (LDP) enable parties to collect aggregate
information about a population while protecting each user's privacy, without relying on a …

Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a
technology for crowdsourcing statistics from end-user client software, anonymously, with …

Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta 1
demonstrates that random shuffling amplifies differential privacy guarantees of locally …

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 …

We give efficient protocols and matching accuracy lower bounds for frequency estimation in
the local model for differential privacy. In this model, individual users randomize their 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 …

The collection and analysis of user data drives improvements in the app and web
ecosystems, but comes with risks to privacy. This paper examines discrete distribution …