Communication-Aware Secret Share Placement in Hierarchical Edge Computing

Hengrun Zhang, Kai Zeng

Abstract: Secret sharing (SS) and secure multiparty computation (MPC) are now widely considered for privacy-preserving data processing. However, related applications can suffer from large transmission overhead. In this article, we propose a communication-aware secret share placement strategy to optimize communication overhead by minimizing transmission hop counts in a hierarchical edge computing architecture. Meanwhile, relevant privacy constraints in SS can still be guaranteed. We show that the constructed optimization problem is NP-hard, and efficient heuristic algorithms can be applied to find suboptimal solutions. With this consideration, we first evaluate two traditional heuristics, i.e., the genetic algorithm (GA) and particle swarm optimization (PSO). Besides, we introduce two basic heuristics, i.e., top-down and bottom-up heuristic, which can outperform GA and PSO in certain cases. Finally, we propose an algorithm, called bottom-up top-down (BUTD) heuristic, which can outperform all of the above four heuristics when communication among different shares of the same secret is comparable to that among different secrets. Comprehensive experimental results demonstrate the advantage of the proposed algorithms.