all InfoSec news
DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
March 15, 2024, 4:10 a.m. | Qiuchen Zhang, Hong kyu Lee, Jing Ma, Jian Lou, Carl Yang, Li Xiong
cs.CR updates on arXiv.org arxiv.org
Abstract: Graph Neural Networks (GNNs) have achieved great success in learning with graph-structured data. Privacy concerns have also been raised for the trained models which could expose the sensitive information of graphs including both node features and the structure information. In this paper, we aim to achieve node-level differential privacy (DP) for training GNNs so that a node and its edges are protected. Node DP is inherently difficult for GNNs because all direct and multi-hop neighbors …
aim arxiv cs.cr cs.lg data decoupled differential privacy expose features graph graphs great information networks neural networks node privacy privacy concerns sensitive sensitive information structure structured data
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Social Engineer For Reverse Engineering Exploit Study
@ Independent study | Remote
Cloud Security Analyst
@ Cloud Peritus | Bengaluru, India
Cyber Program Manager - CISO- United States – Remote
@ Stanley Black & Decker | Towson MD USA - 701 E Joppa Rd Bg 700
Network Security Engineer (AEGIS)
@ Peraton | Virginia Beach, VA, United States
SC2022-002065 Cyber Security Incident Responder (NS) - MON 13 May
@ EMW, Inc. | Mons, Wallonia, Belgium
Information Systems Security Engineer
@ Booz Allen Hamilton | USA, GA, Warner Robins (300 Park Pl Dr)