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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Security and confidentiality of big data stored in the cloud are importantconcerns for many organizations to adopt cloud services. One common approach toaddress the concerns is client-side encryption where data is encrypted on theclient machine before being stored in the cloud. Having encrypted data in thecloud, however, limits the ability of data clustering, which is a crucial partof many data analytics applications, such as search systems. To overcome thelimitation, in this paper, we present an approach named ClustCrypt forefficient topic-based clustering of encrypted unstructured big data in thecloud. ClustCrypt dynamically estimates the optimal number of clusters based onthe statistical characteristics of encrypted data. It also provides clusteringapproach for encrypted data. We deploy ClustCrypt within the context of asecure cloud-based semantic search system (S3BD). Experimental results obtainedfrom evaluating ClustCrypt on three datasets demonstrate on average 60%improvement on clusters' coherency. ClustCrypt also decreases the search-timeoverhead by up to 78% and increases the accuracy of search results by up to 35%

ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud