Blockchain-Based Privacy Preservation Mechanism for Secured Delivery in E-Commerce Systems: Blockchain-Based Privacy Preservation Mechanism for E-Commerce System

With the emergence of IoT, machine learning, and blockchain technology, automated order and delivery systems have shown keen interest from users, suppliers, manufacturers, and the research community. These new technologies can improve the existing order and delivery systems by increasing transparency, removing reliance on third party, improving trust and quality of service, etc. Several efforts have been made to integrate blockchain and other ICT technologies with the existing order delivery systems. However, there are still some challenges that need to be addressed before users and business communities can embrace these technologies. In this work, we identify a few key drivers of the available blockchain and IoT technologies, considering the order delivery system and its implementation for these systems. From the given study, we found that decentralization, trust, cost-value, compliance, and control are benefits that can improve order delivery processes. The security of the users’ data has always been seen as a crucial factor in any public sharing platform. The blockchain model has been considered a solid security platform for many public sharing platforms. In this research, we have contributed a blockchain based hybrid privacy-preserving system for e-commerce systems. The proposed system is comprised of four main components: the user layer, the O2O layer (product order platform, customer delivery platform), the blockchain layer, and the physical delivery layer. The physical delivery layer contains an aggregation platform that is the central place for product company orders and delivery services. The proposed model provides various services, including order reservation, order delivery, and payment. Several experiments are performed for various privacy values to evaluate the performance of each of the privacy scheme under various test parameters. The obtained results revealed that the Laplace privacy approach is the most satiable approach concerning the privacy preservation of customer data. The experiments are then extended and results concerning TPS, Throughput, and latency are obtained and discussed. The results advocate the effectiveness of our proposed model.