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

Graph analysts cannot directly obtain the global structure in decentralizedsocial networks, and analyzing such a network requires collecting local viewsof the social graph from individual users. Since the edges between users mayreveal sensitive social interactions in the local view, applying differentialprivacy in the data collection process is often desirable, which providesstrong and rigorous privacy guarantees. In practical decentralized socialgraphs, different edges have different privacy requirements due to the distinctsensitivity levels. However, the existing differentially private analysis ofsocial graphs provide the same protection for all edges. To address this issue,this work proposes a fine-grained privacy notion as well as novel algorithmsfor private graph analysis. We first design a fine-grained relationshipdifferential privacy (FGR-DP) notion for social graph analysis, which enforcesdifferent protections for the edges with distinct privacy requirements. Then,we design algorithms for triangle counting and k-stars counting, respectively,which can accurately estimate subgraph counts given fine-grained protection forsocial edges. We also analyze upper bounds on the estimation error, includingk-stars and triangle counts, and show their superior performance compared withthe state-of-the-arts. Finally, we perform extensive experiments on two realsocial graph datasets and demonstrate that the proposed mechanisms satisfyingFGR-DP have better utility than the state-of-the-art mechanisms due to thefiner-grained protection.

Graph Analysis in Decentralized Online Social Networks with Fine-Grained Privacy Protection