TRUSTWORTHY AND EFFICIENT BLOCKCHAIN-BASED E-COMMERCE MODEL
Creators: name: Kumar, Valli Sanghami Shankar
Publisher: name: Purdue University Graduate School
Year: 2024
Subjects: classificationCode: 409999
subject: Other engineering not elsewhere classified
classificationCode: 460805
subject: Fairness, accountability, transparency, trust and ethics of computer systems

description: Amidst the rising popularity of digital marketplaces, addressing issues such as non-<br>payment/non-delivery crimes, centralization risks, hacking threats, and the complexity of<br>ownership transfers has become imperative. Many existing studies exploring blockchain<br>technology in digital marketplaces and asset management merely touch upon various application scenarios without establishing a unified platform that ensures trustworthiness and<br>efficiency across the product life cycle. In this thesis, we focus on designing a reliable and efficient e-commerce model to trade various assets. To enhance customer engagement through<br>consensus, we utilize the XGBoost algorithm to identify loyal nodes from the platform entities pool. Alongside appointed nodes, these loyal nodes actively participate in the consensus<br>process. The consensus algorithm guarantees that all involved nodes reach an agreement on<br>the blockchain’s current state. We introduce a novel consensus mechanism named Modified-<br>Practical Byzantine Fault Tolerance (M-PBFT), derived from the Practical Byzantine Fault<br>Tolerance (PBFT) protocol to minimize communication overhead and improve overall efficiency. The modifications primarily target the leader election process and the communication<br>protocols between leader and follower nodes within the PBFT consensus framework.<br>In the domain of tangible assets, our primary objective is to elevate trust among various<br>stakeholders and bolster the reputation of sellers. As a result, we aim to validate secondhand<br>products and their descriptions provided by the sellers before the secondhand products are<br>exchanged. This validation process also holds various entities accountable for their actions.<br>We employ validators based on their location and qualifications to validate the products’<br>descriptions and generate validation certificates for the products, which are then securely<br>recorded on the blockchain. To incentivize the participation of validator nodes and up-<br>hold honest validation of product quality, we introduce an incentive mechanism leveraging<br>Stackelberg game theory.<br>On the other hand, for optimizing intangible assets management, we employ Non-Fungible<br>Tokens (NFT) technology to tokenize these assets. This approach enhances traceability of<br>ownership, transactions, and historical data, while also automating processes like dividend<br>distributions, royalty payments, and ownership transfers through smart contracts. Initially,<br>sellers mint NFTs and utilize the InterPlanetary File System (IPFS) to store the files related<br>to NFTs, NFT metadata, or both since IPFS provides resilience and decentralized storage solutions to our network. The data stored in IPFS is encrypted for security purposes.<br>Further, to aid sellers in pricing their NFTs efficiently, we employ the Stackelberg mechanism. Furthermore, to achieve finer access control in NFTs containing sensitive data and<br>increase sellers’ profits, we propose a Popularity-based Adaptive NFT Management Scheme<br>(PANMS) utilizing Reinforcement Learning (RL). To facilitate prompt and effective asset<br>sales, we design a smart contract-powered auction mechanism.<br>Also, to enhance data recording and event response efficiency, we introduce a weighted<br>L-H index algorithm and transaction prioritization features in the network. The weighted<br>L-H index algorithm determines efficient nodes to broadcast transactions. Transaction prior-<br>itization prioritizes certain transactions such as payments, verdicts during conflicts between<br>sellers and validators, and validation reports to improve the efficiency of the platform. Simulation experiments are conducted to demonstrate the accuracy and efficiency of our proposed<br>schemes.<br>