Preserving privacy while maintaining high utility during sequential publication for data
providers and data users in mathematical statistics, scientific researching, and organizations …

Abstract The dissemination of Electronic Health Records (EHRs) can be extremely beneficial
for multidimensional medical research perspectives leveraging patient diagnoses to reliable …

Privacy violation is a serious issue that must be considered when datasets are released for
public use. To address this issue, a well-known privacy preservation model, l-Diversity, is …

In a real‐world scenario for privacy‐preserving data publishing, the original data are
anonymized and released periodically. Each release may vary in number of records due to …

The continuous release of data, also called serial publication is critical for data analytics but
it can lead to severe privacy disclosures via composition attacks. The serial publication often …

Though anonymization mechanisms have made huge progress in fostering the secondary
use of data, it is still very challenging to obtain adequate knowledge from anonymized data …

There has recently been an increasing need for the collection and sharing of microdata
containing information regarding an individual entity. Because microdata typically contain …

Privacy preservation becomes a more and more serious problem in data publication, which
has drawn dramatic attention in research and development. Recently, several privacy …

In this paper, we study the problem of privacy preserving sequential data publishing for
microdata table. For example, a hospital might release patient's records in every three …

Privacy preservation is an important issue in the release of data for mining purposes.
Recently, a novel l-diversity privacy model was proposed. However, even an l-diverse data …