Jan 23, 2022 · A new privacy-preserving learning framework based on graph neural networks (GNNs), with formal privacy guarantees based on edge local differential privacy.
Towards this objective, we propose Solitude, a new privacy-preserving learning framework based on graph neural networks (GNNs), with formal privacy guarantees ...
Solitude is a new privacy-preserving learning framework based on graph neural networks (GNNs), with formal privacy guarantees based on edge local ...
本项目来源于论文发表在2022年TIFS期刊上： Towards Private Learning on Decentralized Graphs with Local Differential Privacy. 要求. 代码运行要求Python>3.9，同时 ...
This repository is the official implementation of TIFS 2022 paper: Towards Private Learning on Decentralized Graphs with Local Differential Privacy.
Sep 12, 2024 · In this paper, we focus on learning graph neural networks (GNNs) on decentralized social graphs while satisfying local differential privacy (LDP).
Towards Private Learning on Decentralized Graphs With Local Differential Privacy ... to ensure private graph learning on a decentralized network graph.
In this work, we propose a learning framework that can provide node-level privacy, while incurring low utility loss. We focus on a decentralized notion of ...
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In this work, we propose a learning framework that can provide node privacy at the user level, while incurring low utility loss. We focus on a decentralized ...
Nov 21, 2023 · We propose using link local differential privacy over decentralized nodes, enabling collaboration with an untrusted server to train GNNs without revealing the ...