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

Personal information exposed in the Internet is increasing by t he public data opening and sharing, vitalization of SNS(Social Network Service) and growth of information shared between users . Exposed personal information in the Internet can infringe upon targeted users using linkage attack or background attack. To prevent these attack De-identification models were appeared a few years ago. The ’k-anonymity’ has been introduced in the f irst place, and the ‘l-diversity’ and ’t-closeness’ have been followed up as solutions, and diverse algorithms have been bein g suggested for performance improvement nowadays. However, industry or public sectors actually needs a whole solution as a system for the de-identification process rather than performance of the de-identification algorithm. This paper explains a way of de-identification techique for ‘k-anonymity’, ‘ l-diversity’, and ’t-closeness’ algorithm using QI(Quasi-Identifier) grouping met hod in the relational database.Keywords: K-anonymity, L-diversity, T-closeness, De-identification Algorithms

The De-identification Technique Using Data Grouping in Relational Database