Performance Evaluation of Secure Multi-party Computation on Heterogeneous Nodes
This work addresses performance issues in MPC systems for applications like privacy-preserving voting and data analysis, but it appears incremental as it focuses on analysis without proposing new solutions.
The paper analyzed the performance of a secure multi-party computation (MPC) framework, identifying stall times and bottlenecks on homogeneous and heterogeneous compute nodes, but did not report specific numerical results or improvements.
Secure multi-party computation (MPC) is a broad cryptographic concept that can be adopted for privacy-preserving computation. With MPC, a number of parties can collaboratively compute a function, without revealing the actual input or output of the plaintext to others. The applications of MPC range from privacy-preserving voting, arithmetic calculation, and large-scale data analysis. From the system perspective, each party in MPC can run on one compute node. The compute nodes of multiple parties could be either homogeneous or heterogeneous; however, the distributed workloads from the MPC protocols tend to be always homogeneous (symmetric). In this work, we study a representative MPC framework and a set of MPC applications from the system performance perspective. We show the detailed online computation workflow of a state-of-the-art MPC protocol and analyze the root cause of its stall time and performance bottleneck on homogeneous and heterogeneous compute nodes.