Silentflow: Leveraging Trusted Execution for Resource-Limited MPC via Hardware-Algorithm Co-design

TL;DR

Silentflow introduces a TEE-assisted protocol that significantly accelerates secure MPC inference on resource-limited devices by eliminating communication bottlenecks in correlated OT generation through hardware-algorithm co-design.

Contribution

It presents Silentflow, a novel hardware-algorithm co-designed protocol that removes communication in COT generation, enabling real-time secure inference on resource-constrained devices.

Findings

Achieves up to 39.51x speedup over state-of-the-art protocols.

Accelerates PPMLaaS inference on ImageNet with 4.62x and 3.95x speedup over Cryptflow2 and Cheetah.

Effectively balances latency and resource demands through design space exploration.

Abstract

Secure Multi-Party Computation (MPC) offers a practical foundation for privacy-preserving machine learning at the edge, with MPC commonly employed to support nonlinear operations. These MPC protocols fundamentally rely on Oblivious Transfer (OT), particularly Correlated OT (COT), to generate correlated randomness essential for secure computation. Although COT generation is efficient in conventional two-party settings with resource-rich participants, it becomes a critical bottleneck in real-world inference on resource-constrained devices (e.g., IoT sensors and wearables), due to both communication latency and limited computational capacity. To enable real-time secure inference, we introduce Silentflow, a highly efficient Trusted Execution Environment (TEE)-assisted protocol that eliminates communication in COT generation. We tackle the core performance bottleneck-low computational intensity-through structured algorithmic decomposition: kernel fusion for parallelism, Blocked On-chip eXpansion (BOX) to improve memory access patterns, and vectorized batch operations to maximize memory bandwidth utilization. Through design space exploration, we balance end-to-end latency and resource demands, achieving up to 39.51x speedup over state-of-the-art protocols. By offloading COT computations to a Zynq-7000 SoC, SilentFlow accelerates PPMLaaS inference on the ImageNet dataset under resource constraints, achieving a 4.62x and 3.95x speedup over Cryptflow2 and Cheetah, respectively.