Building Secure and Trustworthy Stream Analytics Systems Using Trusted Execution Environment

This thesis enhances secure, efficient data analytics on hybrid cloud-edge platforms using Trusted Execution Environments (TEEs), particularly Intel SGX. It addresses security and latency in IoT-driven analytics by integrating TEEs with cryptographic protocols. The research comprises three studies: firstly, minimizing latency and improving security by processing data at the edge; secondly, creating a secure framework within SGX for efficient stream processing; and thirdly, mitigating side-channel attacks to enhance system performance.