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作 者:Samuel Akwasi Frimpong Mu Han Emmanuel Kwame Effah Joseph Kwame Adjei Isaac Hanson Percy Brown
机构地区:[1]School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang,212013,China [2]Department of Computer Engineering,Ghana Communication Technology University,Accra,PMB 100,Ghana [3]Department of Electrical&Electronic Engineering,Ghana Communication Technology University,Accra,PMB 100,Ghana [4]Department of Computer Science and Information Systems,Ashesi University,Accra,3042,Ghana [5]Department of Telecommunication Engineering,Ghana Communication Technology University,Accra,PMB 100,Ghana
出 处:《Blockchain(Research and Applications)》2024年第4期163-174,共12页区块链研究(英文)
摘 要:This paper addresses the critical challenge of privacy in Online Social Networks(OSNs),where centralized designs compromise user privacy.We propose a novel privacy-preservation framework that integrates blockchain technology with deep learning to overcome these vulnerabilities.Our methodology employs a two-tier architecture:the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm(ePSOGSA)for optimizing feature selection,while the second tier employs an enhanced Non-symmetric Deep Autoencoder(e-NDAE)for anomaly detection.Additionally,a blockchain network secures users’data via smart contracts,ensuring robust data protection.When tested on the NSL-KDD dataset,our framework achieves 98.79%accuracy,a 10%false alarm rate,and a 98.99%detection rate,surpassing existing methods.The integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.
关 键 词:PREPROCESSING Privacy-preservation Blockchain Deep learning Online social network
分 类 号:TP3[自动化与计算机技术—计算机科学与技术]
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