English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Service providers have the ability to collect large amounts of user data. Sometimes, a set of providers may try to aggregate their data for specific data mining tasks. In this process, how to protect users’ privacy is extremely critical. Number o f user write an own novel, own story and own dataset, every user want to keep this data safe on own site and with user internet facility user always search a digital publisher. This is the so-called privacy-preserving collaborative data publishing problem. In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers. We consider a new type of “insider attack” by colluding data providers who may use their own data records (a subset of the overall data) to infer the data records contributed by other data providers. The paper addresses this new threat, and makes several contributions.

Privacy Preserving Utility Verification Security of Data Published by Non Interactive Differentially Private Mechanisms