The massive adoption of The Internet of Things (IoT) and the creation of a smart-world around us leads to several privacy and security concerns. There has been significant work in the past to address the privacy and confidentiality of IoT data such as: providing secure end-to-end channels for the transmission of IoT data, encrypting IoT data using optimized cryptographic schemes such as order-preserving and homomorphic encryption that impose a reasonable energy overhead while improving security. However, for data intensive IoT applications, decrypting large data sets using cryptographic schemes is significantly expensive in terms of latency as seen by the end user of the application.

In this thesis, an Adaptive Latency-Aware Query Processing over encrypted IoT data is proposed that aims to: (i) minimize query latency for data intensive applications as seen by the end user and, (ii) at the same time maintain low energy consumption overhead, comparable to the current schemes as much as possible. This work presents two main contributions: (i) a novel Adaptive latency-aware algorithm which chops down the results of a single large query into several iterations of small sized results by adaptively computing the suitable size (t) of data to be retrieved in each iteration, and (ii) a novel IoT architecture with server cache that implements a latency-hiding technique by establishing concurrency between computation and communication, while leaving the Cloud database unmodified. Both contributions together allow minimizing query latency while maintaining low energy overhead. The effectiveness of the proposed adaptive algorithm is evaluated for latency and energy performance. The results show that the proposed adaptive solution delivers significantly a better latency performance while being comparable to the existing solutions in terms of energy efficiency.