Design and Development of a Secure, Privacy Preserving, and Scalable Blockchain Framework (in English)
Synopsis "Design and Development of a Secure, Privacy Preserving, and Scalable Blockchain Framework (in English)"
Doctoral Thesis / Dissertation from the year 2021 in the subject Computer Science - IT-Security, grade: A, language: English, abstract: Blockchain is an emerging technology, leveraged with features of decentralization, tamper-proof records storage model based on a Peer-to-Peer architecture. It has been mostly applied in financial applications, and extended to various applications, viz., healthcare, supply-chain, and identity management. However, privacy, security, and scalability are the key challenges in blockchain-based applications that immensely limit the extensive adoption of these extended applications. The work in this thesis addresses the aforementioned challenges, while preserving better privacy, security, and scalability in blockchain-based applications. We propose access control techniques to facilitate better privacy, security in the blockchain network and improve scalability off-chain storage model. First, we propose an access control technique to preserve privacy across the blockchain network such that authorized peers can access the resources. Initially, the access control technique is designed for resource privacy. Further, we extend the policy to preserve privacy in blockchain network by categorizing both peers and resources. In this thesis, we also address another important problem, i.e., scalability in blockchain network while storing large size of data in secure off-chain storage. We use a secure distributed off-chain storage model to address the problem of scalability, based on the structure of the blockchain network. Lastly, we propose two-level of privacy preservation approach in blockchain network, while maintaining the scalability in the network. The proposed approach uses BlockCloud-BlockFog structure for deployment and IPFS-based off-chain storage model for large size data storage. All the proposed frameworks are evaluated using real-world data sets.
