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

The singular value decomposition (SVD) is a widely used matrix factorizationtool which underlies plenty of useful applications, e.g. recommendation system,abnormal detection and data compression. Under the environment of emergingInternet of Things (IoT), there would be an increasing demand for data analysisto better human's lives and create new economic growth points. Moreover, due tothe large scope of IoT, most of the data analysis work should be done in thenetwork edge, i.e. handled by fog computing. However, the devices which providefog computing may not be trustable while the data privacy is often thesignificant concern of the IoT application users. Thus, when performing SVD fordata analysis purpose, the privacy of user data should be preserved. Based onthe above reasons, in this paper, we propose a privacy-preserving fog computingframework for SVD computation. The security and performance analysis shows thepracticability of the proposed framework. Furthermore, since differentapplications may utilize the result of SVD operation in different ways, threeapplications with different objectives are introduced to show how the frameworkcould flexibly achieve the purposes of different applications, which indicatesthe flexibility of the design.

A Flexible Privacy-preserving Framework for Singular Value Decomposition under Internet of Things Environment