Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …

The construction of smart cities will bring about a higher quality of life to the masses through
digital interconnectivity, leading to increased efficiency and accessibility in cities. Smart …

How can one build a distributed framework that allows efficient deployment of a wide
spectrum of modern advanced machine learning (ML) programs for industrial-scale …

With the deep penetration of the Internet and mobile devices, privacy preservation in the
local setting has become increasingly relevant. The local setting refers to the scenario where …

With the popularity of smart devices and the widespread use of machine learning methods,
smart edges have become the mainstream of dealing with wireless big data. When smart …

Private record linkage (PRL) is the problem of identifying pairs of records that are similar as
per an input matching rule from databases held by two parties that do not trust one another …

Whether the goal is to estimate the number of people that live in a congressional district, to
estimate the number of individuals that have died in an armed conflict, or to disambiguate …

Corporations, organizations, and governments have collected, digitized, and stored
information in digital forms since the invention of computers, and the speed of such data …

Current solutions for privacy-preserving data sharing among multiple parties either depend
on a centralized authority that must be trusted and provides only weakest-link security (eg …

In this paper, we study the problem of publishing high-dimensional data in a distributed multi-
party environment under differential privacy. In particular, with the assistance of a semi …