Web: http://arxiv.org/abs/2203.00949

Nov. 22, 2022, 2:20 a.m. | Sina Sajadmanesh, Ali Shahin Shamsabadi, Aurélien Bellet, Daniel Gatica-Perez

cs.CR updates on arXiv.org arxiv.org

In this paper, we study the problem of learning Graph Neural Networks (GNNs)
with Differential Privacy (DP). We propose a novel differentially private GNN
based on Aggregation Perturbation (GAP), which adds stochastic noise to the
GNN's aggregation function to statistically obfuscate the presence of a single
edge (edge-level privacy) or a single node and all its adjacent edges
(node-level privacy). Tailored to the specifics of private learning, GAP's new
architecture is composed of three separate modules: (i) the encoder module, …

gap networks neural networks

Senior Cloud Security Engineer

@ HelloFresh | Berlin, Germany

Senior Security Engineer

@ Reverb | Remote, US

I.S. Security Analyst

@ YVFWC | Yakima, WA

Cybersecurity GRC Manager

@ Bitcoin Depot | Remote

Staff, Security Engineer (IT Infra Security Engineering)

@ Coupang | Seoul, South Korea

Principal DevSecOps Engineer (Remote)

@ Raft | Remote

Territory Account Manager - Cybersecurity - Baton Rogue

@ Optiv | Baton Rouge, LA

Analista de Segurança da Informação II (Application Security)

@ Loggi | São Paulo, State of São Paulo, Brazil - Remote

DevSecOps Solutions Architect Lead (AI/ML)

@ Rackner | United States

Senior Cryptography Engineer

@ Copper.co | Remote - UK and Europe

Security Research Manager

@ Nozomi Networks | Italy

Information Security Azure Expert (m/w/d)

@ Roland Berger | Munich, Germany