Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy. (arXiv:2202.10209v2 [cs.CR] UPDATED)

Differentially private GNNs (Graph Neural Networks) have been recently
studied to provide high accuracy in various tasks on graph data while strongly
protecting user privacy. In particular, a recent study proposes an algorithm to
protect each user's feature vector in an attributed graph with LDP (Local
Differential Privacy), a strong privacy notion without a trusted third party.
However, this algorithm does not protect edges (friendships) in a social graph
or protect user privacy in unattributed graphs. How to strongly protect …

differential privacy local networks neural networks privacy response under