{"title":"Locally differentially private graph learning on decentralized social graph","authors":"","doi":"10.1016/j.knosys.2024.112488","DOIUrl":null,"url":null,"abstract":"<div><p>In recent years, decentralized social networks have gained increasing attention, where each client maintains a local view of a social graph. To provide services based on graph learning in such networks, the server commonly needs to collect the local views of the graph structure, which raises privacy issues. In this paper, we focus on learning graph neural networks (GNNs) on decentralized social graphs while satisfying local differential privacy (LDP). Most existing methods collect high-dimensional local views under LDP through Randomized Response, which introduces a large amount of noise and significantly decreases the usability of the collected graph structure for training GNNs. To address this problem, we present Structure Learning-based Locally Private Graph Learning (SL-LPGL). Its main idea is to first collect low-dimensional encoded structural information called cluster degree vectors to reduce the amount of LDP noise, then learn a high-dimensional graph structure from the cluster degree vectors via graph structure learning (GSL) to train GNNs. In SL-LPGL, we propose a Homophily-aware Graph StructurE Initialization (HAGEI) method to provide a low-noise initial graph structure as learning guidance for GSL. We then introduce an Estimated Average Degree Vector Enhanced Graph Structure Learning (EADEGSL) method to further mitigate the negative impact of LDP noise in GSL. We conduct experiments on four real-world graph datasets. The experimental results demonstrate that SL-LPGL outperforms the baselines.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124011225","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, decentralized social networks have gained increasing attention, where each client maintains a local view of a social graph. To provide services based on graph learning in such networks, the server commonly needs to collect the local views of the graph structure, which raises privacy issues. In this paper, we focus on learning graph neural networks (GNNs) on decentralized social graphs while satisfying local differential privacy (LDP). Most existing methods collect high-dimensional local views under LDP through Randomized Response, which introduces a large amount of noise and significantly decreases the usability of the collected graph structure for training GNNs. To address this problem, we present Structure Learning-based Locally Private Graph Learning (SL-LPGL). Its main idea is to first collect low-dimensional encoded structural information called cluster degree vectors to reduce the amount of LDP noise, then learn a high-dimensional graph structure from the cluster degree vectors via graph structure learning (GSL) to train GNNs. In SL-LPGL, we propose a Homophily-aware Graph StructurE Initialization (HAGEI) method to provide a low-noise initial graph structure as learning guidance for GSL. We then introduce an Estimated Average Degree Vector Enhanced Graph Structure Learning (EADEGSL) method to further mitigate the negative impact of LDP noise in GSL. We conduct experiments on four real-world graph datasets. The experimental results demonstrate that SL-LPGL outperforms the baselines.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.