Presentation
TAMI-MPC: Trusted Acceleration of Minimal-Interaction MPC for Efficient Nonlinear Inference
DescriptionSecure multi-party computation (MPC) offers a practical foundation for privacy-preserving machine learning at the edge. However, current MPC systems rely heavily on communication and computation-intensive primitives-such as secure comparison-for nonlinear operations, which are often impractical on resource-constrained platforms. To enable real-time secure inference, we introduce a highly efficient, TEE-accelerated framework for secure comparison. Specifically, we reduce communication cost by redesigning the core primitives-leaf comparison and merge-so that each completes in a single round of interaction, reducing the round complexity from log(n) to just 1 per operation. Furthermore, unlike prior work that heavily relies on Oblivious Transfer (OT), a well-known computational bottleneck, we leverage synchronized seeds inside the TEE to eliminate OT for the vast majority of our designs, along with a correlated-randomness reuse technique that keeps new designs computationally lightweight. To fully realize the potential, we design a specialized accelerator that restructures the dataflow across stages to enable continuous, fine-grained streaming and high parallelism, reducing memory overhead. Our design achieves up to 4.86x speedup on ResNet-50 inference, compared with state-of-the-art CNN frameworks, and achieves up to 7.44x speedup on bert-base inference, compared with state-of-the-art LLM frameworks.
Event Type
Research Manuscript
TimeTuesday, July 284:06pm - 4:18pm PDT
LocationMtg Room 203C