Skip to content

LPGNN: Graph Neural Networks with Local Differential Privacy


The method combines Local Differential Privacy and Graph Neural Networks (GNNs) to preserve the privacy of node features and labels in GNNs, with relatively low privacy cost while providing competitive accuracy, using differential privacy principles.


Sajadmanes, S. and Gatica-Perez, D. (2021) Locally Private Graph Neural Networks. ACM Conference on Computer and Communications Security (CCS)


The method addresses the critically important issue of privacy as it applies to deep learning algorithms on graphs.


Applications involving graph data where nodes features need to be privacy-preserved.

Technology Readiness Level


Contact us for more information

  • Interested in using our technologies?
  • Interested to know more about the licensing possibilities and conditions?

Contact us