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 …

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 …

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 …

We present new practical local differentially private heavy hitters algorithms achieving
optimal or near-optimal worst-case error--TreeHist and Bitstogram. In both algorithms, server …

The notion of Local Differential Privacy (LDP) enables users to respond to sensitive
questions while preserving their privacy. The basic LDP frequent oracle (FO) protocol …

Learning problems form an important category of computational tasks that generalizes many
of the computations researchers apply to large real-life data sets. We ask, What concept …

In recent years, privacy-preserving data mining has been studied extensively, because of
the wide proliferation of sensitive information on the internet. A number of algorithmic …