In today's era, with the rapid development of big data technology and the continuous
increase in data volume, large amounts of data are constantly collected by different …

In this paper, we study the problem of multi-party horizontal split high-dimensional related
data publishing that satisfies differential privacy. The dataset held by each party contains …

In this paper, we study the privacy‐preserving data publishing problem in a distributed
environment. The data contain sensitive information; hence, directly pooling and publishing …

With the rapid advancement of information technology, a large amount of high-value data
have been generated. To exploit the potential value of Big Data and at the same time to …

Aiming at the problem that the existing differential privacy high-dimensional data publishing
methods are difficult to take into account both the complex attribute correlation between data …

A large amount of high-dimensional and heterogeneous data appear in practical
applications, which are often published to third parties for data analysis, recommendations …

Privacy preserving methods supporting for data aggregating have attracted the attention of
researchers in multidisciplinary fields. Among the advanced methods, differential privacy …

Differential privacy has recently emerged in private statistical aggregate analysis as one of
the strongest privacy guarantees. A limitation of the model is that it provides the same …

In local differential privacy (LDP), a challenging problem is the ability to generate high-
dimensional data while efficiently capturing the correlation between attributes in a dataset …

Local differential privacy (LDP) is a state-of-the-art privacy notion that enables terminal
participants to share their private data safely while controlling the privacy disclosure at the …