Spatial decompositions are commonly used in the privacy literature for various purposes
such as range query answering, spatial indexing, count-of-counts histograms, data …

The proposal of local differential privacy solves the problem that the data collector must be
trusted in centralized differential privacy models. The statistical analysis of numerical data …

As a data-centric network, the Mobile Crowd Sensing (MCS) collects and uploads sensing
data through intelligent terminal devices carried by workers. However, due to resource …

Randomized response is one of the oldest and most well-known methods for analyzing
confidential data. However, its utility for differentially private hypothesis testing is limited …

While collecting data from a large population, local differential privacy (LDP), which only
sends users' perturbed data to the data collector, becomes a popular solution to preserving …

In cloud computing, remote accesses over the cloud data inevitably bring the issue of trust.
Despite strong encryption schemes, adversaries can still learn sensitive information from …

In a traffic monitoring system, users report their driving data (eg, routes and timestamps) to a
server, aggregating them to acquire the information of interest and provide services, eg …

Although the Crowd-Sensing perception system brings great data value to people through
the release and analysis of high-dimensional perception data, it causes great hidden danger …

Governments own various types and large amounts of individual data. One governmental
department manages specific areas of data. To develop smart government, data need to be …

This paper establishes the equivalence between Local Differential Privacy (LDP) and a
global limit on learning any knowledge about an object. However, an output from an LDP …