With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

We study the problem of publishing a stream of real-valued data satisfying differential
privacy (DP). One major challenge is that the maximal possible value in the stream can be …

This paper investigates the problem of collecting multidimensional data throughout time (ie,
longitudinal studies) for the fundamental task of frequency estimation under Local …

For protecting users' private data, local differential privacy (LDP) has been leveraged to
provide the privacy-preserving range query, thus supporting further statistical analysis …

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 …

When collecting information, local differential privacy (LDP) alleviates privacy concerns of
users because their private information is randomized before being sent it to the central …

Butterfly counting is widely used to analyze bipartite networks, but counting butterflies in
original bipartite networks can reveal sensitive data and pose a risk of individual privacy …

Differential privacy is a promising approach to formalizing privacy—that is, for writing down
what privacy means as a mathematical equation. This book is provides overview of …

Abundant valuable knowledge can be obtained by learning frequencies of relations in a
decentralized network, which benefits various further complex tasks, such as range query …

In this article, we study the problem of differentially private multi-party data sharing, where
the involved parties assisted by a semi-honest curator collectively generate a shared dataset …