Adaptive Latency-Aware Query Processing on Encrypted Data for the Internet of Things
Reshma Kotamsetty, Manimaran Govindarasu · 2016
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 intense 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 paper, we propose an Adaptive Latency-Aware Query Processing over encrypted IoT data that aims to: (i) minimize query latency for data intense applications as seen by the end user and, (ii) at the same time maintain minimum energy consumption overhead, comparable to the current schemes as much as possible. We present two main contributions: (i) a novel Adaptive latency-aware algorithm which chops down a single large query into several iterations of small sized queries by adaptively computing the optimal query size (t) for each iteration, and (ii) a novel IoT architecture with server cache suitable for addressing the latency issue through parallel execution, while leaving the Cloud database unmodified. Both contributions together allow minimizing query latency while maintaining minimum energy overhead. We evaluated the effectiveness of the proposed adaptive algorithm 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.