QueryGuard: Privacy-preserving Latency-aware Query Optimization for Edge Computing

The emerging edge computing paradigm has enabled applications having low response time requirements to meet the quality of service needs of applications by moving the computations to the edge of the network that is geographically closer to the end-users and end-devices. Despite the low latency advantages provided by the edge computing model, there are significant privacy risks associated with the adoption of edge computing services for applications dealing with sensitive data. In contrast to cloud data centers where system infrastructures are managed through strict and regularized policies, edge computing nodes are scattered geographically and may not have the same degree of regulatory and monitoring oversight. This can lead to higher privacy risks for the data processed and stored at the edge nodes, thus making them less trusted. In this paper, we show that a direct application of traditional performance-based query optimization techniques in edge computing can lead to unexpected data disclosure risks at the edge nodes. We propose a new privacy-preserving latency-aware query optimization framework, QueryGuard, that simultaneously tackles the privacy-aware distributed query processing problem while optimizing the queries for latency. Our experimental evaluation demonstrates that QueryGuard achieves better performance in terms of execution time and memory usage than conventional distributed query optimization techniques while also enforcing the required constraints related to data privacy.