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 ...

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.

Solitude is a new privacy-preserving learning framework based on graph neural networks (GNNs), with formal privacy guarantees based on edge local ...

A new privacy-preserving learning framework based on graph neural networks (GNNs), with formal privacy guarantees based on edge local differential privacy.

Sep 12, 2024 · In LDP, each client locally injects a random perturbation into their real data to protect privacy before sending it to an untrusted server. In ...

Jan 23, 2022 · Many real-world networks are inherently decentralized. For example, in social networks, each user maintains a local view of a social graph, ...

7 days ago · Locally differentially private (LDP) graph analysis allows private analysis on a graph that is distributed across multiple users. However, such ...

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