2024
DOI: 10.29012/jpc.820
|View full text |Cite
|
Sign up to set email alerts
|

Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results

Tamara T. Mueller,
Dmitrii Usynin,
Johannes C. Paetzold
et al.

Abstract: We study the applications of differential privacy (DP) in the context of graph-structured data and discuss the formulations of DP applicable to the publication of graphs and their associated statistics as well as machine learning on graph-based data, including graph neural networks (GNNs). Interpreting DP guarantees in the context of graph-structured data can be challenging, as individual data points are interconnected (often non-linearly or sparsely). This connectivity complicates the computation of individua… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 97 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?