Local differential privacy (LDP), which is a technique that employs unbiased statistical
estimations instead of real data, is usually adopted in data collection, as it can protect every …

In global data analysis, the central server needs the global statistic of the user data stored in
local clients. In such cases, an Honest-but-Curious central server might put user privacy at …

In decentralized settings, the shuffle model of differential privacy has emerged as a
promising alternative to the classical local model. Analyzing privacy amplification via …

The wide application of image classification has given rise to many intelligent systems, such
as face recognition systems, which makes our life more convenient. However, the ensuing …

Voting is used widely to identify a collective decision for a group of agents, based on their
preferences. In this paper, we focus on evaluating and designing voting rules that support …

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

Online service providers benefit from collecting and analyzing preference data from users,
including both implicit preference data (eg, watched videos of a user) and explicit preference …

Increasingly, individuals and companies adopt a cloud service provider as a primary data
and IT infrastructure platform. The remote access of the data inevitably brings the issue of …

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 …

In intelligent transportation systems, the key issue of the Ride-Sharing Service (RSS) is to
find proper drivers for the passengers by Intelligent Matching (IM) of two or three objects …