Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank

Personalized PageRank (PPR) is a fundamental tool in unsupervised learning of graph representations such as node ranking, labeling, and graph embedding. However, while data privacy is one of the most important recent concerns, existing PPR algorithms are not designed to protect user privacy. PPR is highly sensitive to the input graph edges: the difference of only one edge may cause a big change in the PPR vector, potentially leaking private user data.
In this work, we propose an algorithm which …

algorithms cs.cr cs.lg cs.si data data privacy graph important input labeling node privacy private protect sensitive stat.ml tool unsupervised learning user privacy