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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Privacy is one of the most concerned issues in data publishing. Personal data like electronic health records and financial transaction records are usually deemed extremely sensitive although these data can offer significant human benefits if they are analyzed and mined by organizations such as disease research Centre. The emerging research field in data mining, Privacy Preserving Data Publishing (PPDP) (11), targets these challenges. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. It aims at developing techniques that enable publishing data while minimizing data distortion for maintaining utility and ensuring that privacy is preserved. Keywordsprivacy preserving data mining(ppdm), k- anonymity, suppression, generalization ,mapreduce.

Review on Privacy Preservation by Applying Scalable MapReduce BottomUp Generalization (MRBUG) Technique