A deep decentralized privacy-preservation framework for online social networks
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 em...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Blockchain: Research and Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096720924000460 |
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| Summary: | 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. |
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| ISSN: | 2666-9536 |