Marketing data security and privacy protection based on federated gamma in cloud computing environment

Abstract

This study aims to address the existing data security and user privacy vulnerabilities in the “cloud” environment, which is essential for ensuring the safety and integrity of data in the era of big data and cloud computing. To achieve this purpose, we propose a novel approach that combines the logit link function with a longitudinal joint learning framework for the gamma regression model. This approach enhances the application of the model and the loss function, providing a robust solution for data security and user privacy in cloud-based systems. While cloud computing technology has greatly improved the convenience of work and life, it has also introduced significant challenges related to data security and user privacy. This study leverages semantic web technology and blockchain technology to establish a distributed and credit-guaranteed product quality and safety traceability application. By designing a concept verification system and ensuring data integrity at each stage of the product supply chain, this approach addresses these challenges effectively. The distributed network architecture employed in our technical design ensures overall system stability, reliability, and sustainability, with no single point of failure.

Keywords

• Federated learning
• Quality security
• Data security
• System design