Many individuals and companies choose the public cloud as their data and IT infrastructure
platform. But remote accesses over the data inevitably bring the issue of trust. Despite strong …

As big data and data mining technology advance, research on the collection and analysis of
medical data on the internet of medical things (IoMT) has gained increasing attention …

Local differential privacy (LDP) is a promising privacy model for distributed data collection. It
has been widely deployed in real-world systems (eg Chrome, iOS, macOS). In LDP-based …

Abstract Local Differential Privacy (LDP), where each user perturbs her data locally before
sending to an untrusted party, is a new and promising privacy-preserving model. Endorsed …

Introduction: Recently, the tendency of artificial intelligence (AI) and big data
use/applications has been rapidly expanding across the globe, improving people's lifestyles …

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 …

In this paper, we tackle the problem of collecting individual trajectories under local
differential privacy. The key challenge is how to achieve high utility of the collected …

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

In big data era, companies and organizations are keen to collect data from users and
analyse their behaviour patterns to make decisions or predictions for profits. However, it …

We prove that $\mathbb {F} _p $ sketch, a well-celebrated streaming algorithm for frequency
moments estimation, is differentially private as is when $ p\in (0, 1] $. $\mathbb {F} _p …