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/

Zendy Plus

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

Zendy Tools

Zendy Open

Online social networks have enabled new methods and modalities ofcollaboration and sharing. These advances bring privacy concerns: online socialdata is more accessible and persistent and simultaneously less contextualizedthan traditional social interactions. To allay these concerns, many webservices allow users to configure their privacy settings based on a set ofmultiple-choice questions.  We suggest a new paradigm for privacy options. Instead of suggesting the samedefaults to each user, services can leverage knowledge of users' traits torecommend a machine-learned prediction of their privacy preferences forFacebook. As a case study, we build and evaluate MyPrivacy, a publiclyavailable web application that suggests personalized privacy settings. Anevaluation with 199 users shows that users find the suggestions to beappropriate and private; furthermore, they express intent to implement therecommendations made by MyPrivacy. This supports the proposal to putpersonalization to work in online communities to promote privacy and security.

Leveraging Personalization To Facilitate Privacy