Blockchain-based Privacy-Preserving Protocols for Secure IoT Data Sharing: An Implementation Review
Shanmuga Sundaram Palaniswamy, M Jayaprakash, S. Loganathan, E. D. T., N. Savitha, Moorthy Agoramoorthy
Abstract
The Internet of Things, commonly known as IoT, has transformed many sectors by allowing devices to connect and communicate effortlessly. Despite these benefits, this connectivity also leads to major concerns regarding privacy and security, especially when it comes to sensitive information. This paper offers an in-depth review of privacy-centric protocols based on blockchain technology that aim to tackle these issues and protect the sharing of IoT data. The research investigates how different blockchain systems (such as public, private, and consortium types), along with smart contracts, zero-knowledge proofs, and various encryption strategies, can be applied. By analysing numerous case studies and real-life instances, the review assesses how effectively these protocols maintain the confidentiality and integrity of data. It highlights important elements like transaction speed, scalability, and resource allocation. The results suggest that protocols utilizing blockchain provide enhanced data privacy and a lower risk of data breaches compared to conventional methods. For example, smart contracts streamline business transactions, while encryption safeguards data both during transmission and when stored. Nonetheless, issues regarding scalability, integration, and user acceptance still exist. This review offers critical insights for researchers, industry experts, and policymakers focused on enhancing the security of IoT solutions with the help of blockchain technology. This paper evaluates blockchain-based privacy-preserving protocols that facilitate secure IoT data exchange, concentrating on how well they maintain data confidentiality and integrity. By using case studies and practical assessments, the research identifies Hyperledger Fabric as the most effective protocol for IoT applications with high demands. The paper also addresses difficulties concerning scalability, performance, and integration, offering suggestions for future investigation.