Utilizing Blockchain for Privacy Preservation in Internet of Things
- Publication Type:
- Thesis
- Issue Date:
- 2023
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The Internet of Things (IoT) has transformed various sectors, greatly improving efficiency and quality of life through extensive data collection. However, IoT's decentralized and diverse structure raises significant privacy concerns that traditional methods cannot adequately address. This thesis introduces a novel approach to bolstering privacy in IoT using blockchain technology's decentralized, transparent, and unalterable ledger system.
Initially focusing on IoT crowdsourcing, where trust and privacy are critical, the research explores the integration of public and private blockchains for private data collection, laying the groundwork for blockchain's role in IoT privacy. The study then examines blockchain's application in multi-agent IoT systems, which present distinct privacy and security issues. A blockchain-based model is proposed to improve these systems' security, transparency, and resilience.
The research further investigates federated learning in decentralized networks, where privacy and computational challenges persist. A groundbreaking strategy combining public and private blockchains is proposed for secure and private federated learning within the broader Internet of Everything (IoE).
Finally, the thesis presents a novel blockchain-based defense against attacks on federated learning systems, showcasing blockchain's potential as a defense mechanism against a range of privacy and security threats in IoT.
In conclusion, the thesis highlights blockchain's potential to significantly enhance IoT privacy and calls for its wider application, suggesting future research directions for a more secure and trusted IoT environment.
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