Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning. (arXiv:2309.10890v1 [cs.CR])

Graphs are a widely used data structure for collecting and analyzing
relational data. However, when the graph structure is distributed across
several parties, its analysis is particularly challenging. In particular, due
to the sensitivity of the data each party might want to keep their partial
knowledge of the graph private, while still willing to collaborate with the
other parties for tasks of mutual benefit, such as data curation or the removal
of poisoned data. To address this challenge, we propose …

analysis collecting crypto data distributed graphs knowledge link partial party prediction privacy structure