Key Aggregation in Trusted Execution for Multi-Query Privacy in Cloud Computing with Enhancing Data Privacy

The critical challenge of maintaining privacy in multi-query data processing environments, is a major concern, increasingly prevalent in the era of big data. The paper introduces the Key Aggregation in Trusted Execution for Multi-Query Privacy (KATE-MQP) architecture, a novel framework designed to securely handle, process, and analyze data across multiple queries while ensuring stringent privacy preservation. Utilizing differential privacy for key aggregation, the KATE-MQP model represents a significant advancement in the field. Methodologically, the architecture combines Trusted Execution Environments (TEEs) with sophisticated key management. The standout result of the KATE-MQP model is its ability to achieve an increased key aggregation accuracy by 40% while enhancing data privacy, as compared to existing models. Quantitatively, the model demonstrates a marked improvement in processing efficiency as the load increases, demonstrating its scalability. These results underscore the KATE-MQP architecture's potential as a scalable and efficient solution for secure data processing in various applications, from healthcare to finance, where data privacy and integrity are paramount.